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Integrating biological heuristics and gene expression data for gene regulatory network inference

- Zarnegar, Armita, Jelinek, Herbert, Vamplew, Peter, Stranieri, Andrew

  • Authors: Zarnegar, Armita , Jelinek, Herbert , Vamplew, Peter , Stranieri, Andrew
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-10
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  • Description: Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.

Supporting regional aged care nursing staff to manage residents’ behavioural and psychological symptoms of dementia, in real time, using the nurses’ behavioural assistant (NBA) : A pilot site 'end-user attitudes’ trial

- Klein, Britt, Clinnick, Lisa, Chesler, Jessica, Stranieri, Andrew, Bignold, Adam, Dazeley, Richard, McLaren, Suzanne, Lauder, Sue, Balasubramanian, Venki

A genetic algorithm-neural network wrapper approach for bundle branch block detection

- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki

  • Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: Computing in Cardiology Conference (CinC), 2016; Vancouver, BC ;11-14 Sept. 2016, published in Computing in Cardiology p. 461-464
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  • Description: An Electrocardiogram (ECG) records the electrical impulses of the heart and indicates rhythm anomalies for diagnostic purposes [1], [2]. A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment to reduce morbidity and mortality.

Missing data imputation for individualised CVD diagnostic and treatment

- Venkatraman, Sitalakshmi, Yatsko, Andrew, Stranieri, Andrew, Jelinek, Herbert

  • Authors: Venkatraman, Sitalakshmi , Yatsko, Andrew , Stranieri, Andrew , Jelinek, Herbert
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: Computing in Cardiology, 2016 Vol. 43 I E E E Computer Society
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  • Description: Cardiac health screening standards require increasingly more clinical tests consisting of blood, urine and anthropometric measures as well as an extensive clinical and medication history. To ensure optimal screening referrals, diagnostic determinants need to be highly accurate to reduce false positives and ensuing stress to individual patients. However, the data from individual patients partaking in population screening is often incomplete. The current study provides an imputation algorithm that has been applied to patientcentered cardiac health screening. Missing values are iteratively imputed in conjunction with combinations of values on subsets of selected features. The approach was evaluated on the DiabHealth dataset containing 2800 records with over 180 attributes. The results for predicting CVD after data completion showed sensitivity and specificity of 94% and 99% respectively. Removing variables that define cardiac events and associated conditions directly, left ‘age’ followed by ‘use’ of antihypertensive and anti-cholesterol medication, especially statins among the best predictors.

Analysis and comparison of co-occurrence matrix and pixel n-gram features for mammographic images

- Kulkarni, Pradnya, Stranieri, Andrew, Kulkarni, Sid, Ugon, Julien, Mittal, Manish

  • Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Sid , Ugon, Julien , Mittal, Manish
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: International Conference on Communication and Computing p. 7-14
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  • Description: Mammography is a proven way of detecting breast cancer at an early stage. Various feature extraction techniques such as histograms, co-occurrence matrix, local binary patterns, Gabor filters, wavelet transforms are used for analysing mammograms. The novel pixel N-gram feature extraction technique has been inspired from the character N-gram concept of text retrieval. In this paper, we have compared the novel N-gram feature extraction technique with the co-occurrence matrix feature extraction technique. The experiments were conducted on the benchmark miniMIAS mammography database. Classification of mammograms into normal and abnormal category using N-gram features showed promising results with greater classification accuracy, sensitivity and specificity compared to classification using co-occurrence matrix features. Moreover, N-gram features computation are found to be considerably faster than co-occurrence matrix feature computation

Perceptions of façade risks : A preliminary analysis towards the presentation of knowledge graphically

- Edirisinghe, Ruwini, Stranieri, Andrew, Blismas, Nick, Harley, James

  • Authors: Edirisinghe, Ruwini , Stranieri, Andrew , Blismas, Nick , Harley, James
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: CIB W099: Safety and Health in Construction p. 373-382
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  • Description: Prevention through Design (PtD) in construction has been identified as an important factor to improve Workplace Health and Safety (WHS). However, challenges exist implementing PtD in practice due to technical, social and regulatory complexity. Moreover, WHS is poorly embedded in curricula of design professionals who generally have limited experience of construction methodologies. Attempts to assist designers with the relevant knowledge in the past have been limited to generic risk assessment guides, sample databases, or static knowledge-based systems. We propose that a graphical knowledge based information visualisation device, an infographic, can cue designers to consider relevant knowledge. Façade design is selected as the case study of the project, which involves the development of an infographic and experimental evaluation to determine its impact. The first phase of the project covered the development of the infographic, however this paper reports the findings related to the second phase of this ongoing project; the experimental evaluation of the infographic. A Q-methodology was selected and administered to a group to determine the subjectivity inherent in façade design risk perceptions prior to the introduction of the infographic to the same group in a workshop environment. 27 participants including designers/architects, engineers, contractors and safety professionals were recruited for the project. Each participant was asked to sort photographs of 16 different façade systems into five categories ranging from safest to least safe. The participants were asked to consider the construction risks associated with the façade design presented in each photo and to provide reasons for their sort selection. Preliminary data analysis of the whole population of data is presented in this paper and a rationale for the common agreements among the whole group is investigated. Further analysis including group-level and detailed quantitative analysis are ongoing.

Energy-efficient priority-based routing scheme for the healthcare wireless sensor networks

- Saeed, Ather, Stranieri, Andrew, Dazeley, Richard

  • Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard
  • Date: 2014
  • Type: Text , Conference paper
  • Relation: 9th WSEAS International Conference on Remote Sensing, Budapest 10/12/2103 pg 19-27
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  • Description: Abstract: - In time-critical and data intensive applications, efficient acquisition of sensitive datasets is a challenge because of network congestion, void regions and node failures that commonly occur in wireless sensor networks (WSN), while monitoring the wellbeing of patients with serious medical conditions. The sensor devices attached to such patients are used for monitoring the vital signs of those with serious heart problems, Parkinson disease, Epilepsy and high blood pressure. This paper typically focuses on the reliable acquisition of datasets and provides a fault-tolerant priority based routing scheme with Dynamic Jumping (FTMPR-DJ) for the energy-efficient acquisition and dissemination of datasets. A new fault-tolerant scheme has been proposed that will significantly minimize data loss and network congestion and is well supported with extensive experiments to show effectiveness of the proposed routing scheme.

A participatory information management framework for patient centred care of autism spectrum disorder

- De Silva, Daswin, Burstein, Frada, Stranieri, Andrew, Williams, Katrina, Rinehart, Nicole

Association of ankle brachial pressure index with heart rate variability in a rural screening clinic

- Jelinek, Herbert, De Silva, Daswin, Burstein, Frada, Stranieri, Andrew, Khalaf, Kinda, Khandoker, Ahsan, Al-Aubaidy, Hayder

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

- Kelarev, Andrei, Stranieri, Andrew, Abawajy, Jemal, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Abawajy, Jemal , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: Tenth Australasian Data Mining Conference Vol. 134, p. 93-101
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  • Description: This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

The role of emotional intelligence on the resolution of disputes involving the electronic health record

- Bellucci, Emilia, Venkatraman, Sitalakshmi, Muecke, Nial, Stranieri, Andrew

A reasoning framework for decision making in water allocation: a tree for water

- Graymore, Michelle, Mays, Heather, Stranieri, Andrew, Lehmann, La Vergne, McRae-Williams, Pamela, Thoms, Gavin, Yearwood, John

Decision support based needs assessment for cancer patients

- Stranieri, Andrew, Kulkarni, Siddhivinayak, Macfadyen, Alyx, Love, Anthony, Vaughan, Stephen

  • Authors: Stranieri, Andrew , Kulkarni, Siddhivinayak , Macfadyen, Alyx , Love, Anthony , Vaughan, Stephen
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: Australasian workshop on health informatics and knowledge management (HIKM)
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  • Description: Regular assessment of wellness or quality of life for patients throughout a cancer journey is important so as to identify aspects of life that could lead to distress and impede recovery or acceptance. The emerging trends in assessment are to deploy validated, quality of life instruments on touchscreen computers in medical waiting rooms. However, these add to workload of health care professionals and can be impersonal for patients to use. In this article, an alternate approach is presented that involves a decision support system with natural dialogue that elicits the patient's specific context in a far finer grained manner than is possible with questionnaire based instruments. The system includes a model of heuristics that health care professionals in a locality use to make inferences regarding a patient's quality of life and avenues for referral.
  • Description: E1

Online group deliberation for the elicitation of shared values to underpin decision making

- Feldman, Yishai, Kraft, Donald, Kuflik, Tsvi, Afshar, Faezeh, Stranieri, Andrew, Yearwood, John

  • Authors: Feldman, Yishai , Kraft, Donald , Kuflik, Tsvi , Afshar, Faezeh , Stranieri, Andrew , Yearwood, John
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 7th International Conference, NGITS 2009, Next generation information technologies and systems, Haifa, Israel : 16th-18th June 2009 Vol. 5831, p. 158-168
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  • Description: Values have been shown to underpin our attitudes, behaviour and motivate our decisions. Values do not exist in isolation but have meaning in relation to other values. However, values are not solely the purview of individuals as communities and organisations have core values implicit in their culture, policies and practices. Values for a group can be determined by a minority in power, derived by algorithmically merging values each group member holds, or set by deliberative consensus. The elicitation of values for the group by deliberation is likely to lead to widespread acceptance of values arrived at, however enticing individuals to engage in face to face discussion about values has been found to be very difficult. We present an online deliberative communication approach for the anonymous deliberation of values and claim that the framework has the elements required for the elicitation of shared values.
  • Description: 2003007509

A scenario-based learning environment for critical care nursing

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2005
  • Type: Text , Conference paper
  • Relation: Paper presented at HIC 2005: Thirteenth National Health Informatic Conference, 31 July-2 August 2005, Melbourne, Australia, Melbourne : 31st July, 2005
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  • Description: Narrative or story telling has long been used to structure and organise human experience. In contrast to logical models of reasoning, narrative models enable complex situations to be understood and recalled by humans readily. In this work a narrative model is integrated into a logical reasoning model for the purpose of advancing a learning environment that promises to be engaging and effective. The narrative model includes a representation of the point of a story and a simple story grammar. The logical reasoning model is based on an argumentation model. The learning environment is designed to enable the automated generation of plausible scenarios that involves an intensive care unit nurse responding to a low oxygen level alarm.
  • Description: E1
  • Description: 2003001434

The integration of narrative and argumentation for a scenario-based learning environment in law

- Stranieri, Andrew, Yearwood, John

  • Authors: Stranieri, Andrew , Yearwood, John
  • Date: 2005
  • Type: Text , Conference paper
  • Relation: Paper presented at the Tenth International Conference on Artificial Intelligence and Law, Bologna, Italy : 6th - 11th June, 2005
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  • Description: Narrative or story telling has long been used to structure and organise human experience. In contrast to logical models of reasoning, narrative models enable complex situations to be understood and recalled by humans readily. There is also some indication that narrative models represent the way in which jurors weigh up the veracity of legal evidence. In this work a narrative model is integrated into a logical reasoning model for the purpose of advancing a learning environment that promises to be engaging and effective. The narrative model includes a representation of the point of a story and a simple story grammar. The learning environment is designed to enable the automated generation of plausible scenarios representing a variety of family law property division cases told from the point of view of numerous characters.
  • Description: E1
  • Description: 2003001432

Association rules and multiple variables in complex times series forecasting

- Bertoli, Marcello, Stranieri, Andrew, Banerjee, Arunava

Forecasting on complex datasets with association rules

- Bertoli, Marcello, Stranieri, Andrew

  • Authors: Bertoli, Marcello , Stranieri, Andrew
  • Date: 2004
  • Type: Text , Conference paper
  • Relation: Paper presented at Knowledge-Based Intelligent Information & Engineering Systems: 8th International Conference, KES 2004, Proceedings, Part I, Wellington, New Zealand : 21st September, 2004
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  • Description: Forecasting in complex fields such as financial markets or national economies is made difficult by the impact of numerous variables with unknown inter-dependencies. A forecasting approach is presented that produces forecasts on a variable based on past values for that variable and other, possibly inter-dependent variables. The approach is based on the intuition that the next value in a series depends on the last value and the last two values and the last three values and so on. Furthermore, the next value depends also on past values on other variables. No assumptions about the form of functions underpinning a dataset are made. Rather, evidence for each possible next value is collected by combining confidence values of numerous association rules. The approach has been evaluated by forecasting values in a hypothetical dataset and by forecasting the direction of the Australian stock market index with favorable results.
  • Description: E1
  • Description: 2003000849

Scenario-based learning environments

- Yearwood, John, Stranieri, Andrew

Web-based decision support for structured reasoning in health

- Stranieri, Andrew, Yearwood, John, Gervasoni, Susan, Garner, Susan, Deans, Cecil, Johnstone, Alistair

  • Authors: Stranieri, Andrew , Yearwood, John , Gervasoni, Susan , Garner, Susan , Deans, Cecil , Johnstone, Alistair
  • Date: 2004
  • Type: Text , Conference paper
  • Relation: Paper presented at Health Informatics Conference 2004 - Let's make a difference with health ICT, Brisbane, Queensland : 25th July, 2004
  • Full Text: false
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  • Description: Decision making processes in the health care setting are often complex, demanding of professionals and rapidly changing. Furthermore, there is increasing pressure for professionals to make reasoning transparent and consistent. Decision support technologies have not made a substantial contribution to these issues to date largely because knowledge is difficult to elicit and maintain and existing development tools are very sophisticated yet complex. In this study a method for representing complex and discretionary reasoning used successfully in law was applied to the task of modelling decision making processes that critical care nurses deploy in responding to a low oxygen alarm. The approach, based on decision and argument tree diagrams enables the rapid development of small scale, yet useful web based decision support systems.
  • Description: E1
  • Description: 2003000831