Visual tools for analysing evolution, emergence, and error in data streams
- Hart, Sol, Yearwood, John, Bagirov, Adil
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
- Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
- Full Text:
- Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
- Description: 2003005432
A smart proxy for a next generation web services transaction
- Pradhan, Sunam, Zaslavsky, Arkady
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
- Authors: Pradhan, Sunam , Zaslavsky, Arkady
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 646-651
- Full Text:
- Description: In this paper, we propose and describe sProxy - smart proxy, a software tool in Web Services transaction. sProxy acts as a gateway between transaction management systems and Web services which implements a key abstraction of proxy management systems. This enables to perform transactions in the loosely coupled environment i.e. loose coupling among services. Proxies are useful to invoke Web services to allow an easy programming model that facilitates the serialization and transmission of service invocations. Our proposed model supports relaxation of traditional ACID properties with existing commit and recovery protocols. The model works on non-ACID type of transactions which encapsulates Web services. It also uses multithreading proxies to check and update transaction simultaneously. The proposed model solves the current problems with distributed computational activities which involves both transactions and Web Services. The proposed model is more abstract and generic as demonstrated in the paper.
- Description: 2003005442
Predicting Australian stock market index using neural networks exploiting dynamical swings and intermarket influences
- Pan, Heping, Tilakaratne, Chandima, Yearwood, John
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
- Full Text:
- Reviewed:
- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440
- Authors: Pan, Heping , Tilakaratne, Chandima , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 43-55
- Full Text:
- Reviewed:
- Description: This paper presents a computational approach for predicting the Australian stock market index AORD using multi-layer feed-forward neural networks front the time series data of AORD and various interrelated markets. This effort aims to discover an effective neural network, or a set of adaptive neural networks for this prediction purpose, which can exploit or model various dynamical swings and inter-market influences discovered from professional technical analysis and quantitative analysis. Within a limited range defined by our empirical knowledge, three aspects of effectiveness on data selection are considered: effective inputs from the target market (AORD) itself, a sufficient set of interrelated markets,. and effective inputs from the interrelated markets. Two traditional dimensions of the neural network architecture are also considered: the optimal number of hidden layers, and the optimal number of hidden neurons for each hidden layer. Three important results were obtained: A 6-day cycle was discovered in the Australian stock market during the studied period; the time signature used as additional inputs provides useful information; and a basic neural network using six daily returns of AORD and one daily, returns of SP500 plus the day of the week as inputs exhibits up to 80% directional prediction correctness.
- Description: C1
- Description: 2003001440
Using links to aid web classification
- Xie, Wei, Mammadov, Musa, Yearwood, John
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
- Full Text:
- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
- Authors: Xie, Wei , Mammadov, Musa , Yearwood, John
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 981-986
- Full Text:
- Description: In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
- Description: 2003005438
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
- Abawajy, Jemal, Kelarev, Andrei, Chowdhury, Morshed, Stranieri, Andrew, Jelinek, Herbert
- Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
- Description: C1
- Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
- Description: C1
A new scoring system in Cystic Fibrosis : Statistical tools for database analysis - A preliminary report
- Hafen, Gaudenz, Hurst, Cameron, Yearwood, John, Smith, Julie, Dzalilov, Zari, Robinson, P. J.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
A fully automated CAD system using multi-category feature selection with restricted recombination
- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Mukherjee, Subhasis
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
- Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
- Full Text:
- Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
- Description: 2003005429
Image retrieval based on fuzzy mapping of image database and fuzzy similarity distance
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
- Full Text:
- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
- Authors: Kulkarni, Siddhivinayak
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 812-817
- Full Text:
- Description: The on-line image retrieval process consists of a query example image, given by the user as an input, from which low-level image features are extracted. These image features are used to find images in the database which are most similar to the query image. A drawback, however, is that these low level image features are often too restricted to describe images on a conceptual or semantic level. The gap between the high level query from the user and low level features extracted by a computer is known as the semantic gap. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. This paper proposes a novel fuzzy approach for mapping the fuzzy database while extracting the colour features from image and assigning the weights to this fuzzy content when calculating the similarity between the query image and the images in database. Number of experiments was conducted on a small colour image database and promising results were obtained.
- Description: 2003005444
Decisions surrounding adverse drug reaction prescribing : Insights from consumers and implications for decision support
- O'Brien, Michelle, Yearwood, John
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 57-71
- Full Text:
- Reviewed:
- Description: This paper presents findings from case studies of health consumers who each suspect they may have experienced an adverse drug reaction (ADR). These case studies are part of a larger study involving consumer/doctor decisions surrounding suspected adverse drug reactions and prescribing. Decision support to assist with the diagnosis and management of ADRs has, to date, primarily focused on providing in-time information to prescribers about factors that pertain to the consumer and the medications they are taking. Decision support that includes consumers usually targets treatment decisions. The results of this paper indicate the prescriber is only one decision contributor in a rich tapestry of decision contributors and decision types, and consumer decision types are significantly broader than treatment decisions. The results provide guidance for the development of decision support within this domain.
- Description: C1
- Description: 2003001435
- Authors: O'Brien, Michelle , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Research and Practice in Information Technology Vol. 37, no. 1 (2005), p. 57-71
- Full Text:
- Reviewed:
- Description: This paper presents findings from case studies of health consumers who each suspect they may have experienced an adverse drug reaction (ADR). These case studies are part of a larger study involving consumer/doctor decisions surrounding suspected adverse drug reactions and prescribing. Decision support to assist with the diagnosis and management of ADRs has, to date, primarily focused on providing in-time information to prescribers about factors that pertain to the consumer and the medications they are taking. Decision support that includes consumers usually targets treatment decisions. The results of this paper indicate the prescriber is only one decision contributor in a rich tapestry of decision contributors and decision types, and consumer decision types are significantly broader than treatment decisions. The results provide guidance for the development of decision support within this domain.
- Description: C1
- Description: 2003001435
An Internet-based guided self-help intervention for panic symptoms: Randomized controlled trial
- Van Ballegooijen, Wouter, Riper, Heleen, Klein, Britt, Ebert, David, Kramer, Jeannet, Meulenbeek, Peter, Cuijpers, Pim
- Authors: Van Ballegooijen, Wouter , Riper, Heleen , Klein, Britt , Ebert, David , Kramer, Jeannet , Meulenbeek, Peter , Cuijpers, Pim
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 15, no. 7 (2013), p.
- Full Text:
- Reviewed:
- Description: Background: Internet-based guided self-help is efficacious for panic disorder, but it is not known whether such treatment is effective for milder panic symptoms as well. Objective: To evaluate the effectiveness of Don't Panic Online, an Internet-based self-help course for mild panic symptoms, which is based on cognitive behavioral principles and includes guidance by email. Methods: A pragmatic randomized controlled trial was conducted. Participants (N=126) were recruited from the general population and randomized to either the intervention group or to a waiting-list control group. Inclusion criteria were a Panic Disorder Severity Scale-Self Report (PDSS-SR) score between 5-15 and no suicide risk. Panic symptom severity was the primary outcome measure; secondary outcome measures were anxiety and depressive symptom severity. Measurements were conducted online and took place at baseline and 12 weeks after baseline (T1). At baseline, diagnoses were obtained by telephone interviews. Results: Analyses of covariance (intention-to-treat) showed no significant differences in panic symptom reduction between groups. Completers-only analyses revealed a moderate effect size in favor of the intervention group (Cohen's d=0.73, P=.01). Only 27% of the intervention group finished lesson 4 or more (out of 6). Nonresponse at T1 was high for the total sample (42.1%). Diagnostic interviews showed that many participants suffered from comorbid depression and anxiety disorders. Conclusions: The Internet-based guided self-help course appears to be ineffective for individuals with panic symptoms. However, intervention completers did derive clinical benefits from the intervention. © Wouter van Ballegooijen, Heleen Riper, Britt Klein, David Daniel Ebert, Jeannet Kramer, Peter Meulenbeek, Pim Cuijpers.
- Description: C1
- Authors: Van Ballegooijen, Wouter , Riper, Heleen , Klein, Britt , Ebert, David , Kramer, Jeannet , Meulenbeek, Peter , Cuijpers, Pim
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 15, no. 7 (2013), p.
- Full Text:
- Reviewed:
- Description: Background: Internet-based guided self-help is efficacious for panic disorder, but it is not known whether such treatment is effective for milder panic symptoms as well. Objective: To evaluate the effectiveness of Don't Panic Online, an Internet-based self-help course for mild panic symptoms, which is based on cognitive behavioral principles and includes guidance by email. Methods: A pragmatic randomized controlled trial was conducted. Participants (N=126) were recruited from the general population and randomized to either the intervention group or to a waiting-list control group. Inclusion criteria were a Panic Disorder Severity Scale-Self Report (PDSS-SR) score between 5-15 and no suicide risk. Panic symptom severity was the primary outcome measure; secondary outcome measures were anxiety and depressive symptom severity. Measurements were conducted online and took place at baseline and 12 weeks after baseline (T1). At baseline, diagnoses were obtained by telephone interviews. Results: Analyses of covariance (intention-to-treat) showed no significant differences in panic symptom reduction between groups. Completers-only analyses revealed a moderate effect size in favor of the intervention group (Cohen's d=0.73, P=.01). Only 27% of the intervention group finished lesson 4 or more (out of 6). Nonresponse at T1 was high for the total sample (42.1%). Diagnostic interviews showed that many participants suffered from comorbid depression and anxiety disorders. Conclusions: The Internet-based guided self-help course appears to be ineffective for individuals with panic symptoms. However, intervention completers did derive clinical benefits from the intervention. © Wouter van Ballegooijen, Heleen Riper, Britt Klein, David Daniel Ebert, Jeannet Kramer, Peter Meulenbeek, Pim Cuijpers.
- Description: C1
Multiple comorbidities of 21 psychological disorders and relationships with psychosocial variables: A study of the online assessment and diagnostic system within a web-based population
- Al-Asadi, Ali, Klein, Britt, Meyer, Denny
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 17, no. 2 (2015), p. 355
- Full Text:
- Reviewed:
- Description: Background: While research in the area of e-mental health has received considerable attention over the last decade, there are still many areas that have not been addressed. One such area is the comorbidity of psychological disorders in a Web-based sample using online assessment and diagnostic tools, and the relationships between comorbidities and psychosocial variables. Objective: We aimed to identify comorbidities of psychological disorders of an online sample using an online diagnostic tool. Based on diagnoses made by an automated online assessment and diagnostic system administered to a large group of online participants, multiple comorbidities (co-occurrences) of 21 psychological disorders for males and females were identified. We examined the relationships between dyadic comorbidities of anxiety and depressive disorders and the psychosocial variables sex, age, suicidal ideation, social support, and quality of life. Methods: An online complex algorithm based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision, was used to assign primary and secondary diagnoses of 21 psychological disorders to 12,665 online participants. The frequency of co-occurrences of psychological disorders for males and females were calculated for all disorders. A series of hierarchical loglinear analyses were performed to examine the relationships between the dyadic comorbidities of depression and various anxiety disorders and the variables suicidal ideation, social support, quality of life, sex, and age. Results: A 21-by-21 frequency of co-occurrences of psychological disorders matrix revealed the presence of multiple significant dyadic comorbidities for males and females. Also, for those with some of the dyadic depression and the anxiety disorders, the odds for having suicidal ideation, reporting inadequate social support, and poorer quality of life increased for those with two-disorder comorbidity than for those with only one of the same two disorders. Conclusions: Comorbidities of several psychological disorders using an online assessment tool within a Web-based population were similar to those found in face-to-face clinics using traditional assessment tools. Results provided support for the transdiagnostic approaches and confirmed the positive relationship between comorbidity and suicidal ideation, the negative relationship between comorbidity and social support, and the negative relationship comorbidity and quality of life. © 2015, Journal of Medical Internet Research. All rights reserved.
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 17, no. 2 (2015), p. 355
- Full Text:
- Reviewed:
- Description: Background: While research in the area of e-mental health has received considerable attention over the last decade, there are still many areas that have not been addressed. One such area is the comorbidity of psychological disorders in a Web-based sample using online assessment and diagnostic tools, and the relationships between comorbidities and psychosocial variables. Objective: We aimed to identify comorbidities of psychological disorders of an online sample using an online diagnostic tool. Based on diagnoses made by an automated online assessment and diagnostic system administered to a large group of online participants, multiple comorbidities (co-occurrences) of 21 psychological disorders for males and females were identified. We examined the relationships between dyadic comorbidities of anxiety and depressive disorders and the psychosocial variables sex, age, suicidal ideation, social support, and quality of life. Methods: An online complex algorithm based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision, was used to assign primary and secondary diagnoses of 21 psychological disorders to 12,665 online participants. The frequency of co-occurrences of psychological disorders for males and females were calculated for all disorders. A series of hierarchical loglinear analyses were performed to examine the relationships between the dyadic comorbidities of depression and various anxiety disorders and the variables suicidal ideation, social support, quality of life, sex, and age. Results: A 21-by-21 frequency of co-occurrences of psychological disorders matrix revealed the presence of multiple significant dyadic comorbidities for males and females. Also, for those with some of the dyadic depression and the anxiety disorders, the odds for having suicidal ideation, reporting inadequate social support, and poorer quality of life increased for those with two-disorder comorbidity than for those with only one of the same two disorders. Conclusions: Comorbidities of several psychological disorders using an online assessment tool within a Web-based population were similar to those found in face-to-face clinics using traditional assessment tools. Results provided support for the transdiagnostic approaches and confirmed the positive relationship between comorbidity and suicidal ideation, the negative relationship between comorbidity and social support, and the negative relationship comorbidity and quality of life. © 2015, Journal of Medical Internet Research. All rights reserved.
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 13, no. 131 (2012), p. 1-16
- Full Text:
- Reviewed:
- Description: Abstract Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
- Date: 2012
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 13, no. 131 (2012), p. 1-16
- Full Text:
- Reviewed:
- Description: Abstract Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
Posttreatment attrition and its predictors, attrition bias, and treatment efficacy of the anxiety online programs
- Al-Asadi, Ali, Klein, Britt, Meyer, Denny
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 16, no. 10 (2014), p. e232
- Full Text:
- Reviewed:
- Description: Background: Although relatively new, the field of e-mental health is becoming more popular with more attention given to researching its various aspects. However, there are many areas that still need further research, especially identifying attrition predictors at various phases of assessment and treatment delivery. Objective: The present study identified the predictors of posttreatment assessment completers based on 24 pre- and posttreatment demographic and personal variables and 1 treatment variable, their impact on attrition bias, and the efficacy of the 5 fully automated self-help anxiety treatment programs for generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD). Methods: A complex algorithm was used to diagnose participants' mental disorders based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision; DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders were offered an online 12-week disorder-specific treatment program. A total of 3199 individuals did not formally drop out of the 12-week treatment cycle, whereas 142 individuals formally dropped out. However, only 347 participants who completed their treatment cycle also completed the posttreatment assessment measures. Based on these measures, predictors of attrition were identified and attrition bias was examined. The efficacy of the 5 treatment programs was assessed based on anxiety-specific severity scores and 5 additional treatment outcome measures. Results: On average, completers of posttreatment assessment measures were more likely to be seeking self-help online programs; have heard about the program from traditional media or from family and friends; were receiving mental health assistance; were more likely to learn best by reading, hearing and doing; had a lower pretreatment Kessler-6 total score; and were older in age. Predicted probabilities resulting from these attrition variables displayed no significant attrition bias using Heckman's method and thus allowing for the use of completer analysis. Six treatment outcome measures (Kessler-6 total score, number of diagnosed disorders, self-confidence in managing mental health issues, quality of life, and the corresponding pre- and posttreatment severity for each program-specific anxiety disorder and for major depressive episode) were used to assess the efficacy of the 5 anxiety treatment programs. Repeated measures MANOVA revealed a significant multivariate time effect for all treatment outcome measures for each treatment program. Follow-up repeated measures ANOVAs revealed significant improvements on all 6 treatment outcome measures for GAD and PTSD, 5 treatment outcome measures were significant for SAD and PD/A, and 4 treatment outcome measures were significant for OCD. Conclusions: Results identified predictors of posttreatment assessment completers and provided further support for the efficacy of self-help online treatment programs for the 5 anxiety disorders
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 16, no. 10 (2014), p. e232
- Full Text:
- Reviewed:
- Description: Background: Although relatively new, the field of e-mental health is becoming more popular with more attention given to researching its various aspects. However, there are many areas that still need further research, especially identifying attrition predictors at various phases of assessment and treatment delivery. Objective: The present study identified the predictors of posttreatment assessment completers based on 24 pre- and posttreatment demographic and personal variables and 1 treatment variable, their impact on attrition bias, and the efficacy of the 5 fully automated self-help anxiety treatment programs for generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder with or without agoraphobia (PD/A), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD). Methods: A complex algorithm was used to diagnose participants' mental disorders based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision; DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders were offered an online 12-week disorder-specific treatment program. A total of 3199 individuals did not formally drop out of the 12-week treatment cycle, whereas 142 individuals formally dropped out. However, only 347 participants who completed their treatment cycle also completed the posttreatment assessment measures. Based on these measures, predictors of attrition were identified and attrition bias was examined. The efficacy of the 5 treatment programs was assessed based on anxiety-specific severity scores and 5 additional treatment outcome measures. Results: On average, completers of posttreatment assessment measures were more likely to be seeking self-help online programs; have heard about the program from traditional media or from family and friends; were receiving mental health assistance; were more likely to learn best by reading, hearing and doing; had a lower pretreatment Kessler-6 total score; and were older in age. Predicted probabilities resulting from these attrition variables displayed no significant attrition bias using Heckman's method and thus allowing for the use of completer analysis. Six treatment outcome measures (Kessler-6 total score, number of diagnosed disorders, self-confidence in managing mental health issues, quality of life, and the corresponding pre- and posttreatment severity for each program-specific anxiety disorder and for major depressive episode) were used to assess the efficacy of the 5 anxiety treatment programs. Repeated measures MANOVA revealed a significant multivariate time effect for all treatment outcome measures for each treatment program. Follow-up repeated measures ANOVAs revealed significant improvements on all 6 treatment outcome measures for GAD and PTSD, 5 treatment outcome measures were significant for SAD and PD/A, and 4 treatment outcome measures were significant for OCD. Conclusions: Results identified predictors of posttreatment assessment completers and provided further support for the efficacy of self-help online treatment programs for the 5 anxiety disorders
Pretreatment attrition and formal withdrawal during treatment and their predictors: An exploratory study of the anxiety online data
- Al-Asadi, Ali, Klein, Britt, Meyer, Denny
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 16, no. 6 (2014), p. e152
- Full Text:
- Reviewed:
- Description: Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program.
- Description: Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program
- Authors: Al-Asadi, Ali , Klein, Britt , Meyer, Denny
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 16, no. 6 (2014), p. e152
- Full Text:
- Reviewed:
- Description: Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program.
- Description: Although in its infancy, the field of e-mental health interventions has been gaining popularity and afforded considerable research attention. However, there are many gaps in the research. One such gap is in the area of attrition predictors at various stages of assessment and treatment delivery. Objective: This exploratory study applied univariate and multivariate analysis to a large dataset provided by the Anxiety Online (now called Mental Health Online) system to identify predictors of attrition in treatment commencers and in those who formally withdrew during treatment based on 24 pretreatment demographic and personal variables and one clinical measure. Methods: Participants were assessed using a complex online algorithm that resulted in primary and secondary diagnoses in accordance with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). Those who received a primary or secondary diagnosis of 1 of 5 anxiety disorders (generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and panic disorder) were offered an online 12-week disorder-specific treatment program. Results: Of 9394 potential participants, a total of 3880 clients enrolled and 5514 did not enroll in one of the treatment programs following the completion of pretreatment assessment measures (pretreatment attrition rate: 58.70%). A total of 3199 individuals did not formally withdraw from the 12-week treatment cycle, whereas 142 individuals formally dropped out (formal withdrawal during treatment dropout rate of 4.25%). The treatment commencers differed significantly (P<.001-.03) from the noncommencers on several variables (reason for registering, mental health concerns, postsecondary education, where first heard about Anxiety Online, Kessler-6 score, stage of change, quality of life, relationship status, preferred method of learning, and smoking status). Those who formally withdrew during treatment differed significantly (P=.002-.03) from those who did not formally withdraw in that they were less likely to express concerns about anxiety, stress, and depression; to rate their quality of life as very poor, poor, or good; to report adequate level of social support; and to report readiness to make or were in the process of making changes. Conclusions: This exploratory study identified predictors of pretreatment attrition and formal withdrawal during treatment dropouts for the Anxiety Online program
A model of the circadian clock in the cyanobacterium Cyanothece sp. ATCC 51142
- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Gaudana, Sandeep, Wangikar, Pramod
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
- Full Text:
- Reviewed:
- Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.
- Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Gaudana, Sandeep , Wangikar, Pramod
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (Supplement 2) (2013), p. s14-1-s14-9
- Full Text:
- Reviewed:
- Description: Background The over consumption of fossil fuels has led to growing concerns over climate change and global warming. Increasing research activities have been carried out towards alternative viable biofuel sources. Of several different biofuel platforms, cyanobacteria possess great potential, for their ability to accumulate biomass tens of times faster than traditional oilseed crops. The cyanobacterium Cyanothece sp. ATCC 51142 has recently attracted lots of research interest as a model organism for such research. Cyanothece can perform efficiently both photosynthesis and nitrogen fixation within the same cell, and has been recently shown to produce biohydrogen--a byproduct of nitrogen fixation--at very high rates of several folds higher than previously described hydrogen-producing photosynthetic microbes. Since the key enzyme for nitrogen fixation is very sensitive to oxygen produced by photosynthesis, Cyanothece employs a sophisticated temporal separation scheme, where nitrogen fixation occurs at night and photosynthesis at day. At the core of this temporal separation scheme is a robust clocking mechanism, which so far has not been thoroughly studied. Understanding how this circadian clock interacts with and harmonizes global transcription of key cellular processes is one of the keys to realize the inherent potential of this organism. Results In this paper, we employ several state of the art bioinformatics techniques for studying the core circadian clock in Cyanothece sp. ATCC 51142, and its interactions with other key cellular processes. We employ comparative genomics techniques to map the circadian clock genes and genetic interactions from another cyanobacterial species, namely Synechococcus elongatus PCC 7942, of which the circadian clock has been much more thoroughly investigated. Using time series gene expression data for Cyanothece, we employ gene regulatory network reconstruction techniques to learn this network de novo, and compare the reconstructed network against the interactions currently reported in the literature. Next, we build a computational model of the interactions between the core clock and other cellular processes, and show how this model can predict the behaviour of the system under changing environmental conditions. The constructed models significantly advance our understanding of the Cyanothece circadian clock functional mechanisms.
An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy
- Stranieri, Andrew, Abawajy, Jemal, Kelarev, Andrei, Huda, Shamsul, Chowdhury, Morshed, Jelinek, Herbert
- Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
- Full Text:
- Reviewed:
- Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
- Description: 2003011130
- Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
- Full Text:
- Reviewed:
- Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
- Description: 2003011130
Chemical characterization of MEA degradation in PCC pilot plants operating in Australia
- Cruickshank, Alicia, Verheyen, Vincent, Adeloju, Samuel, Meuleman, Erik, Chaffee, Alan, Cottrell, Aaron, Feron, Paul
- Authors: Cruickshank, Alicia , Verheyen, Vincent , Adeloju, Samuel , Meuleman, Erik , Chaffee, Alan , Cottrell, Aaron , Feron, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: Energy Procedia Vol. 37, no. (2013), p. 877-882
- Full Text:
- Reviewed:
- Description: An important step towards commercial scale post-combustion CO2 capture from coal-fired power stations is understanding solvent degradation. Laboratory scale trials have identified three main solvent degradation pathways for 30% MEA: oxidative degradation, carbamate polymerization and formation of heat stable salts. This paper probes the semi-volatile organic compounds produced from a single batch of 30% MEA which was used to capture CO2 from a black coal-fired power station (Tarong, Queensland, Australia) for approximately 700 hours, followed by 500 hours at the brown coal-fired power station (Loy Yang, Victoria, Australia). Comparisons are made between the compounds identified in this aged solvent system with MEA degradation reactions described in literature. Most of semi-volatile compounds tentatively identified by GC/MS have previously been reported in laboratory scale degradation trials. Our preliminary results show low levels of degradation products were present in samples after its use in the pilot plant at Tarong (black coal) and consequent 13 months storage, but much higher concentrations were later found in the same solvent during its at use in the pilot plant at Loy Yang Power (brown coal). Further work includes identifying the cause of poor GC/MS repeatability and investigating the relative rates of reactions described in literature. The impact of inorganic anions and dissolved metals on MEA degradation will also be explored.
- Authors: Cruickshank, Alicia , Verheyen, Vincent , Adeloju, Samuel , Meuleman, Erik , Chaffee, Alan , Cottrell, Aaron , Feron, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: Energy Procedia Vol. 37, no. (2013), p. 877-882
- Full Text:
- Reviewed:
- Description: An important step towards commercial scale post-combustion CO2 capture from coal-fired power stations is understanding solvent degradation. Laboratory scale trials have identified three main solvent degradation pathways for 30% MEA: oxidative degradation, carbamate polymerization and formation of heat stable salts. This paper probes the semi-volatile organic compounds produced from a single batch of 30% MEA which was used to capture CO2 from a black coal-fired power station (Tarong, Queensland, Australia) for approximately 700 hours, followed by 500 hours at the brown coal-fired power station (Loy Yang, Victoria, Australia). Comparisons are made between the compounds identified in this aged solvent system with MEA degradation reactions described in literature. Most of semi-volatile compounds tentatively identified by GC/MS have previously been reported in laboratory scale degradation trials. Our preliminary results show low levels of degradation products were present in samples after its use in the pilot plant at Tarong (black coal) and consequent 13 months storage, but much higher concentrations were later found in the same solvent during its at use in the pilot plant at Loy Yang Power (brown coal). Further work includes identifying the cause of poor GC/MS repeatability and investigating the relative rates of reactions described in literature. The impact of inorganic anions and dissolved metals on MEA degradation will also be explored.
Learning the naive bayes classifier with optimization models
- Taheri, Sona, Mammadov, Musa
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Applied Mathematics and Computer Science Vol. 23, no. 4 (2013), p. 787-795
- Full Text:
- Reviewed:
- Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
- Authors: Taheri, Sona , Mammadov, Musa
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Applied Mathematics and Computer Science Vol. 23, no. 4 (2013), p. 787-795
- Full Text:
- Reviewed:
- Description: Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
Calibration of an articulated CMM using stochastic approximations
- Sultan, Ibrahim, Puthiyaveettil, Prajeesh
- Authors: Sultan, Ibrahim , Puthiyaveettil, Prajeesh
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Advanced Manufacturing Technology Vol. 63, no. 1-4 (2012), p. 201-207
- Full Text:
- Reviewed:
- Description: A coordinate measuring machine (CMM) is meant to digitise the spatial locations of points and feed the resulting measurements to a CAD system for storing and processing. For reliable utilisation of a CMM, a calibration procedure is often undertaken to eliminate the inaccuracies which result from manufacturing, assembly and installation errors. In this paper, an Immersion digitizer coordinate measuring machine has been calibrated using an accurately manufactured master cuboid fixture. This CMM has been designed as an articulated manipulator to enhance its dexterity and versatility. As such, the calibration problem is tackled with the aid of a kinematic model similar to those employed for the analysis of serial robots. In addition, a stochastic-based optimisation technique is used to identify the parameters of the kinematic model in order for the accurate performance to be achieved. The experimental results demonstrate the effectiveness of this method, whereby the measuring accuracy has been improved considerably. © 2012 Springer-Verlag London Limited.
- Description: 2003010394
- Authors: Sultan, Ibrahim , Puthiyaveettil, Prajeesh
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Advanced Manufacturing Technology Vol. 63, no. 1-4 (2012), p. 201-207
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- Description: A coordinate measuring machine (CMM) is meant to digitise the spatial locations of points and feed the resulting measurements to a CAD system for storing and processing. For reliable utilisation of a CMM, a calibration procedure is often undertaken to eliminate the inaccuracies which result from manufacturing, assembly and installation errors. In this paper, an Immersion digitizer coordinate measuring machine has been calibrated using an accurately manufactured master cuboid fixture. This CMM has been designed as an articulated manipulator to enhance its dexterity and versatility. As such, the calibration problem is tackled with the aid of a kinematic model similar to those employed for the analysis of serial robots. In addition, a stochastic-based optimisation technique is used to identify the parameters of the kinematic model in order for the accurate performance to be achieved. The experimental results demonstrate the effectiveness of this method, whereby the measuring accuracy has been improved considerably. © 2012 Springer-Verlag London Limited.
- Description: 2003010394
Incorporating time-delays in S-System model for reverse engineering genetic networks
- Chowdhury, Ahsan, Chetty, Madhu, Nguyen, Vinh
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (2013), p. 1-22
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- Description: Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2013
- Type: Text , Journal article
- Relation: BMC Bioinformatics Vol. 14, no. (2013), p. 1-22
- Full Text:
- Reviewed:
- Description: Background In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously. As a result, all these approaches cannot detect important interactions of the other type. S-System model, a differential equation based approach which has been increasingly applied for modeling GRNs, also suffers from this limitation. In fact, all S-System based existing modeling approaches have been designed to capture only instantaneous interactions, and are unable to infer time-delayed interactions. Results In this paper, we propose a novel Time-Delayed S-System (TDSS) model which uses a set of delay differential equations to represent the system dynamics. The ability to incorporate time-delay parameters in the proposed S-System model enables simultaneous modeling of both instantaneous and time-delayed interactions. Furthermore, the delay parameters are not limited to just positive integer values (corresponding to time stamps in the data), but can also take fractional values. Moreover, we also propose a new criterion for model evaluation exploiting the sparse and scale-free nature of GRNs to effectively narrow down the search space, which not only reduces the computation time significantly but also improves model accuracy. The evaluation criterion systematically adapts the max-min in-degrees and also systematically balances the effect of network accuracy and complexity during optimization. Conclusion The four well-known performance measures applied to the experimental studies on synthetic networks with various time-delayed regulations clearly demonstrate that the proposed method can capture both instantaneous and delayed interactions correctly with high precision. The experiments carried out on two well-known real-life networks, namely IRMA and SOS DNA repair network in Escherichia coli show a significant improvement compared with other state-of-the-art approaches for GRN modeling.