A robust ensemble regression model for reconstructing genetic networks
- Authors: Gamage, Hasini , Chetty, Madhu , Lim, Suryani , Hallinan, Jennifer , Nguyen, Huy
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
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- Description: Genetic networks contain important information about biological processes, including regulatory relationships and gene-gene interactions. Numerous methods, using high-dimensional gene expression data have been developed to capture these interactions. These gene expression data, generated using high-throughput technologies, are prone to noise. However, most existing network inference methods are unable to cope with noisy data, making genetic network reconstruction challenging. In this paper, we propose a novel ensemble regression model combining quantile regression and cross-validated Ridge regression, RidgeCV, to infer interactions from noisy gene expression data. The application of quantile regression to GRN inference is novel, and its design makes it appropriate for noisy data. RidgeCV also addresses other important issues, such as data overfitting and multicollinearity. First, each regression method is independently applied to gene expression data and the output of these methods, in the form of ranked gene lists, is aggregated using a novel gene score-based method by considering the gene rank and model importance. The model importance score is evaluated based on an adjusted coefficient of determination. This method implicitly includes majority voting by averaging each gene score value across all models. The proposed model was tested on the DREAM4 datasets and publicly available small-scale real-world network datasets. Experiments with noisy datasets showed that the proposed ensemble model is more accurate and efficient than other state-of-the-art methods. © 2023 IEEE.
Meaning-sensitive text data augmentation with intelligent masking
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Intelligent Systems and Technology Vol. 14, no. 6 (2023), p.
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- Description: With the recent popularity of applying large-scale deep neural network-based models for natural language processing (NLP), attention to develop methods for text data augmentation is at its peak, since the limited size of training data tends to significantly affect the accuracy of these models. To this end, we propose a novel text data augmentation technique called Intelligent Masking with Optimal Substitutions Text Data Augmentation (IMOSA). IMOSA, developed for labelled sentences, can identify the most favourable sentences and locate the appropriate word combinations in a particular sentence to replace and generate synthetic sentences with a meaning closer to the original sentence, while also significantly increasing the diversity of the dataset. We demonstrate that the proposed technique notably improves the performance of classifiers based on attention-based transformer models through the extensive experiments for five different text classification tasks which are performed under the low data regime in a context-Aware NLP setting. The analysis clearly shows that IMOSA effectively generates more sentences using favourable original examples and completely ignores undesirable examples. Furthermore, the experiments carried out confirm IMOSA's ability to add diversity to the augmented dataset using multiple distinct masking patterns against the same original sentence, which remarkably adds variety to the training dataset. IMOSA consistently outperforms the two key masked language model-based text data augmentation techniques, and demonstrates a robust performance against the critical challenging NLP tasks. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Combining kinetic orders for efficient S-System modelling of gene regulatory network
- Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
- Date: 2022
- Type: Text , Journal article
- Relation: BioSystems Vol. 220, no. (2022), p.
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- Description: S-System models, non-linear differential equation models, are widely used for reconstructing gene regulatory networks from temporal gene expression data. An S-System model involves two states, generation and degeneration, and uses the kinetic parameters gij and hij, to represent the direction, nature, and intensity of the genetic interactions. The need for learning a large number of model parameters results in increased computational expense. Previously, we improved the performance of the algorithm using dynamic allocation of the maximum in-degree for each gene. While the method was effective for smaller networks, a large amount of computation was still needed for larger networks. This problem arose mainly due to the increased occurrence of invalid networks during optimization, primarily because the two kinetic parameters (gij and hij) of the S-System model converge independently during optimization. Being independent, these two parameters can converge to values that can indicate contradictory gene interactions, specifically inhibition or activation. In this study, to address this major challenge in S-System modelling, we developed a novel method that includes two features: a penalty term that penalizes those networks with invalid kinetic orders, and a parameter, wij, derived by combining the kinetic parameters gij and hij. The novel penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm. Rather than remaining constant, it is dynamic, with its magnitude dependent on the number of invalid interactions in the given network. This approach encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner. The previous DRNI method, a two-stage approach which uses dynamic allocation of the maximum in-degree for each gene, was further improved by adding a third stage which applies the proposed wij to handle the invalid regulations that may still exist in that candidate solutions. The method was tested on different gene expression datasets, and was able to reduce the number of iterations and produce improved network accuracies. For a 20 gene network, the number of generations required for convergence was reduced by 300, and the F-score improved by 0.05 compared to our previously reported DRNI approach. For the well-known 10 gene networks of the DREAM challenge, our method produced an improvement in the average area under the ROC curve of the DREAM4 10 gene networks. © 2022
Ensemble regression modelling for genetic network inference
- Authors: Gamage, Hasini , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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- Description: An accurate reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is crucial for discovering complex biological interactions. Among many different approaches for inferring GRNs, there are several methods which produce high false positive interactions, and are unstable, requiring fine tuning for many of their parameters. In this paper, we consider the GRN inference problem as a regression problem, and propose a simple ensemble regression-based feature selection model which is a combination of cross-validated Lasso and cross-validated Ridge algorithms for reconstructing GRNs. Due to the novelty of the proposed ensemble model, it is able to eliminate overfitting, multi co-linearity issues, and irrelevant genes within one computational approach. While observing the type of gene-gene regulatory interactions the regression model also identifies the direction of these interactions. A new coefficient of determination (R2)-based approach identifies the best model to fit the data among LassoCV and RidgeCV, and evaluates the model importance in term of gene-wise maximum in-degree which decides the maximum number of regulatory genes including self-regulations that can be selected from a given method. Then, an evaluated gene score-based majority voting technique aggregates the selected gene lists from each method. In our experiments, the performance of the proposed ensemble approach was evaluated using gene expression datasets from three small-scale real gene networks. Our proposed model outperformed other state-of-the-art methods, producing high true positives, reducing false positives, and obtaining high Structural Accuracy, while maintaining model stability and efficiency. © 2022 IEEE.
Incorporating price information in Blockchain-based energy trading
- Authors: Islam, Ezazul , Chetty, Madhu , Lim, Suryani , Chadhar, Mehmood , Islam, Syed
- Date: 2022
- Type: Text , Conference proceedings
- Relation: SIG SAND -Systems Analysis and Design, 2022; Minneapolis; August 10th-14th, 2022 in AMCIS 2022 Proceedings. 6.
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- Description: Blockchain-based peer-to-peer (P2P) ecosystem is well suited for distributed energy trading as it is inherently decentralised. In a distributed energy trading, an auctioneer passes unspent reservations to the next auctioneer, as dictated by the passing mechanism. However, traditional P2P energy trading systems used passing mechanisms that only partially consider the auction capability of the next auctioneer. We propose iPass, which incorporates price information when passing unspent auction reservations in P2P energy trading environment. The three performance metrics applied to measure the trading efficiency are (a) auction convergence time, (b) the number of auction settlements, and (c) the economic surplus of buyers and sellers. We simulated the proposed mechanism in Hyperledger Fabric, a permissioned blockchain framework. Hyperledger Fabric manages the data storage and smart contracts. Experiments show iPass is more efficient compared to existing passing mechanisms.
Integrating steady-state and dynamic gene expression data for improving genetic network modelling
- Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, Ottawa Canada, 15-17 August 2022, 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022
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- Description: Reverse engineering of Gene Regulatory Networks (GRNs) from experimentally obtained high-throughput data is an active and promising area of research. Among several modelling techniques, the S-System model, a set of tightly coupled differential equations, mimics the complexities and dynamics of biochemical systems, and thus provides realistic GRN representation. While it offers mathematical flexibility and biological relevance, the high number of learning parameters can lead to a computational burden. In our earlier work, we addressed this issue by judicious use of prior knowledge. However, another major cause of computational load is the need for numerical integration of the differential equations for the estimation of S-system model parameters. In this paper, we propose a method to obtain initial model parameter values from the steady state of the system, thereby computing simpler and less complex algebraic equations compared to the regular differential equations of S-systems. These network parameters are input as prior knowledge for the optimization of the dynamic S-System using differential equations. The proposed framework includes a novel fitness evaluation for steady-state S-System models, a novel evolutionary parameter learning framework, and a technique to incorporate the candidate solutions in dynamic S-System modelling. Our proposed methodology reached optimal model parameter values quickly, requiring only one-third of the fitness function evaluations, compared to our previously reported DRNI (Dynamically regulated network initialization) method for S-System modelling. © 2022 IEEE.
Resilience of stablecoin reserve for distributed energy trading
- Authors: Islam, Md Ezazul , Chetty, Madhu , Lim, Suryani , Chadhar, Mehmood , Islam, Syed
- Date: 2022
- Type: Text , Conference paper
- Relation: 14th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022, Melbourne, Australia, 20-23 November 2022, Asia-Pacific Power and Energy Engineering Conference, APPEEC Vol. 2022-November
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- Description: For payment settlement, a blockchain-based Peer-To-Peer (P2P) energy trading requires a stable medium of exchange with little price volatility. Stablecoins, the most suitable medium of exchange, gaining concentration even from central banks. A consortium of central banks recommends complying with capital and liquidity standards for the high-quality liquid asset (HQLA) for the solvency of banks or financial institutions. Stablecoin as HQLA requires the adaption of such standards in P2P energy trading for reserve resilience. We propose a mechanism (NF90) that controls the inflow of stablecoins responding to Liquidity Coverage Ratio (LCR) for reserve resilience. Basel III accord recommends 100% of LCR. We measure the efficiency of NF90 concerning LCR as a metric. We simulate the proposed mechanism in Hyperledger Fabric as a permissioned blockchain platform for decentralisation, data storage, and smart contract. Experiments show that NF90 is the most efficient inflow control mechanism compared to other simulated mechanisms. © 2022 IEEE.
An improved memetic approach for protein structure prediction incorporating maximal hydrophobic core estimation concept
- Authors: Nazmul, Rumana , Chetty, Madhu , Chowdhury, Ahsan
- Date: 2021
- Type: Text , Journal article
- Relation: Knowledge-Based Systems Vol. 219, no. (2021), p. 104395
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- Description: Protein Structure Prediction (PSP) from the primary amino acid sequence, even using a simplified Hydrophobic-Polar (HP) lattice model, continues to be extremely challenging. Finding an optimal conformation, even for a small sequence, by any of the currently known evolutionary approaches is computationally extensive and time consuming. Although Memetic Algorithms (MAs) have shown success in finding the optimal solution for PSP, no significant work on the incorporation of domain or problem specific knowledge into the search process to significantly improve their performance is reported. In this paper, we present an approach to incorporate such knowledge into the initial population to enhance the effectiveness of MA for PSP. The domain knowledge we propose to use is based on the concept of maximal ‘core’ formation by exploiting the fundamental property of the H residues to be at the core of the minimum energy optimal protein structure. A generic technique is proposed for estimating the maximal Hydrophobic core (H-core) in a protein sequence for 2D Square, 3D Cubic and a more complex and realistic 3D FCC (Face Centered Cubic) lattice models. Subsequently, the knowledge of this estimated core is incorporated in an MA. The experiments conducted using HP benchmark sequences for 2D Square, 3D Cubic and 3D FCC lattice models show that the proposed MA with the new core-based population initialization technique has superior performance to the existing methods in terms of convergence speed as well as minimal energy. © 2018 Elsevier B.V.
Cost effective annotation framework using zero-shot text classification
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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- Description: Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality. © 2021 IEEE.
Dynamically regulated initialization for S-system modelling of genetic networks
- Authors: Gill, Jaskaran , Chetty, Madhu , Shatte, Adrian , Hallinan, Jennifer
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, Virtual, Online, 13-15 October 2021, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021
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- Description: Reverse engineering of gene regulatory networks through temporal gene expression data is an active area of research. Among the plethora of modelling techniques under investigation is the decoupled S-system model, which attempts to capture the non-linearity of biological systems in detail. For the model, number of parameters to be estimated are significantly high even when the network is of small or medium scale. Thus, the inference process poses a significant computational burden. In this paper, we propose: (1) a novel population initialization technique, Dynamically Regulated Prediction Initialization (DRPI), which utilises prior knowledge of biological gene expression data to create a feedback loop to produce dynamically regulated high-quality individuals for initial population; (2) an adaptive fitness function; and (3) a method for the maintenance of population diversity. The aim of this work is to reduce the computational complexity of the inference algorithm, to speed up the entire process of reverse engineering. The performance of the proposed algorithm was evaluated against a benchmark dataset and compared with other methods from earlier work. The experimental results show that we succeeded in achieving higher accuracy results in lesser fitness evaluations, considerably reducing the computational burden of the inference process. © 2021 IEEE.
Multimodal memetic framework for low-resolution protein structure prediction
- Authors: Nazmul, Rumana , Chetty, Madhu , Chowdhury, Ashan
- Date: 2020
- Type: Text , Journal article
- Relation: Swarm and Evolutionary Computation Vol. 52, no. (Feb 2020), p. 14
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- Description: In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the "nearest peak". The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.
Pre-trained language models with limited data for intent classification
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Karmakar, Gour , Walls, Darren
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 International Joint Conference on Neural Networks, IJCNN 2020
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- Description: Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-bystep methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements. © 2020 IEEE.
Large scale modeling of genetic networks using gene knockout data
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 Australasian Computer Science Week Multiconference, ACSW 2018; Brisbane, Australia; 29th January-2nd February 2018; published in ACM International Conference Proceedings Series
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- Description: Gene regulatory network (GRN) represents a set of genes and their regulatory interactions. The inference of the regulatory interactions between genes is usually carried out as an optimization problem using an appropriate mathematical model and the time-series gene expression data. Among the various models proposed for GRN inference, our recently proposed Michaelis-Menten kinetics based ODE model provides a good trade-off between the computational complexity and biological relevance. This model, like other known GRN models, also uses an evolutionary algorithm for parameter estimation. Since the search space for large networks is huge, leading to a low accuracy of inference, it is important to reduce the search region for improved performance of the optimization algorithm. In this paper, we propose a classification method using gene knockout data to eliminate a large infeasible region from the optimization search area. We also propose a method for partial inference of regulations when all the regulators of a given regulated gene are unregulated genes. The proposed method is evaluated by reconstructing in silico networks of large sizes. © 2018 ACM.
Modeling neurocognitive reaction time with gamma distribution
- Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ACSW'18 . Proceedings of the Australasian Computer Science Week Multiconference; Brisbane, QLD; January 2018; Article 28 p. 1-10
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- Description: As a broader effort to build a holistic biopsychosocial health metric, reaction time data obtained from participants undertaking neurocognitive tests have been examined using Exploratory Data Analysis (EDA) for assessing its distribution. Many of the known existing methods assume, that the reaction time data follows a Gaussian distribution and thus commonly use statistical measures such as Analysis of Variance (ANOVA) for analysis. However, it is not mandatory for the reaction time data, to necessarily follow Gaussian distribution and in many instances, it can be better modeled by other representations such as Gamma distribution. Unlike Gaussian distribution which is defined using mean and variance, the Gamma distribution is defined using shape and scale parameters which also considers higher order moments of data such as skewness and kurtosis. Generalized Linear Models (GLM), based on the family exponential distributions such as Gamma distribution, which have been used to model reaction time in other domains, have not been fully explored for modeling reaction time data in psychology domain. While limited use of Gamma distribution have been reported [5, 17, 21], for analyzing response times, their application has been somewhat ad-hoc rather than systematic. For this proposed research, we use a real life biopsychosocial dataset, generated from the 'digital health' intervention programs conducted by the Faculty of Health, Federation University, Australia. The two digital intervention programs were the 'Mindfulness' program and 'Physical Activity' program. The neurocognitive tests were carried out as part of the 'Mindfulness' program. In this paper, we investigate the participants' reaction time distributions in neurocognitive tests such as the Psychology Experiment Building Language (PEBL) Go/No-Go test [19], which is a subset of the larger biopsychosocial data set. PEBL is an open source software system for designing and running psychological experiments. Analysis of participants' reaction time in the PEBL Go/No-Go test, shows that the reaction time data are more compatible with a Gamma distribution and clearly demonstrate that these can be better modeled by Gamma distribution.
Relevance of frequency of heart-rate peaks as indicator of ‘Biological’ Stress level
- Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ICONIP 2018 International on Neural Information Processing; Siem Reap, Cambodia; 13th-16th December, 2018 p. 598-609
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- Description: The biopsychosocial (BPS) model proposes that health is best understood as a combination of bio-physiological, psychological and social determinants, and thus advocates for a far more comprehensive investigation of the relationships between ‘mind-body’ health. For this holistic analysis, we need a suitable measure to indicate participants’ ‘biological’ stress. With the advent of wearable sensor devices, health monitoring is becoming easier. In this study, we focus on bio-physiological indicators of stress, from wearable devices using the heart-rate data. The analysis of such heart-rate data presents a set of practical challenges. We review various measures currently in use for stress measurement and their relevance and significance with the wearables’ heart-rate data. In this paper, we propose to use the novel ‘peak heart-rate count’ metric to quantify level of ‘biological’ stress. Real life biometric data obtained from digital health intervention program was considered for the study. Our study indicates the significance of using frequency of ‘peak heart-rate count’ as a ‘biological’ stress measure.
Exploiting temporal genetic correlations for enhancing regulatory network optimization
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 23rd International Conference on Neural Information Processing, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing (Lecture Notes in Computer Science series) Vol. 9947 LNCS, p. 479-487
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- Description: Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference. © Springer International Publishing AG 2016.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Decoupled modeling of gene regulatory networks using Michaelis-Menten kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A set of genes and their regulatory interactions are represented in a gene regulatory network (GRN). Since GRNs play a major role in maintaining the cellular activities, inferring these networks is significant for understanding biological processes. Among the models available for GRN reconstruction, our recently developed nonlinear model [1] using Michaelis-Menten kinetics is considered to be more biologically relevant. However, the model remains coupled in the current form making the process computationally expensive, especially for large GRNs. In this paper, we enhance the existing model leading to a decoupled form which not only speeds up the computation, but also makes the model more realistic by representing the strength of each regulatory arc by a distinct Michaelis-Menten constant. The parameter estimation is carried out using differential evolution algorithm. The model is validated by inferring two synthetic networks. Results show that while the accuracy of reconstruction is similar to the coupled model, they are achieved at a faster speed. © Springer International Publishing Switzerland 2015.
Evaluating influence of microRNA in reconstructing gene regulatory networks
- Authors: Chowdhury, Ahsan , Chetty, Madhu , Nguyen, Vinh
- Date: 2015
- Type: Text , Journal article
- Relation: Cognitive neurodynamics Vol. 8, no. 3 (2015), p. 251-9
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- Description: Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.
Frequency decomposition based gene clustering
- Authors: Rahman, Md Abdur , Chetty, Madhu , Bulach, Dieter , Wangikar, Pramod
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9490, p. 170-181
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- Description: Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network. © Springer International Publishing Switzerland 2015.
Gene regulatory network inference using Michaelis-Menten kinetics
- Authors: Youseph, Ahammed , Chetty, Madhu , Karmakar, Gour
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2015 IEEE Congress on Evolutionary Computation (Cec); Sendai, Japan; 25th-28th May 2015 p. 2392-2397
- Full Text: false
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- Description: A gene regulatory network (GRN) represents a collection of genes, connected via regulatory interactions. Reverse engineering GRNs is a challenging problem in systems biology. Various models have been proposed for modeling GRNs. However, many of these models lack the capability to explain the molecular mechanisms underlying the biological process. Michaelis-Menten kinetics can be used to model the biomolecular mechanisms and is a widely used non-linear approach to represent biochemical systems. However, the model in its current form is not suitable for reverse engineering biological systems. In this paper, based on Michaelis-Menten kinetics, we develop a new model to reverse engineer GRNs. The parameter estimation is formulated as an optimization problem which is solved by adapting trigonometric differential evolution (TDE), a variant of differential evolution (DE). The model is applied for reconstructing both in silico and in vivo networks. The results are promising and as the model is fully biologically relevant, it provides a new perspective for accurate GRN inference.