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Degree of differential prioritization

- Ooi, Chia, Chetty, Madhu, Teng, Shyh

  • Authors: Ooi, Chia , Chetty, Madhu , Teng, Shyh
  • Date: 2009
  • Type: Text , Journal article
  • Relation: IEEE Engineering in Medicine and Biology Magazine Vol. 28, no. 4 (2009), p. 45-51
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  • Description: Because of the high dimensionality of the microarray data sets, feature selection (FS) has become an important challenge in molecular classification. Using the degree of differential prioritization (DDP) between relevance and antiredundancy, our proposed DDP-based FS technique is capable of achieving better accuracies than those previously reported, using a smaller predictor set. However, previously, we have neither devised nor used any method for determining the value of the DDP to be used for the data set of interest before the FS process. In this article, we propose a system for predicting the optimal value of the DDP, which costs less computationally than conventional tuning while maintaining the independence of the FS technique from the type of underlying classifier used

Modelling gene regulatory networks using computational intelligence techniques

- Ram, Ramesh, Chetty, Madhu

  • Authors: Ram, Ramesh , Chetty, Madhu
  • Date: 2010
  • Type: Text , Book chapter
  • Relation: Handbook of Research on Computational Methodologies in Gene Regulatory Networks p. 244-265
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  • Description: This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.

Improving gene regulatory network inference using network topology information

- Nair, Ajay, Chetty, Madhu, Wangikar, Pramod

  • Authors: Nair, Ajay , Chetty, Madhu , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Molecular BioSystems Vol. 11, no. 9 (2015), p. 2449-2463
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  • Description: Inferring the gene regulatory network (GRN) structure from data is an important problem in computational biology. However, it is a computationally complex problem and approximate methods such as heuristic search techniques, restriction of the maximum-number-of-parents (maxP) for a gene, or an optimal search under special conditions are required. The limitations of a heuristic search are well known but literature on the detailed analysis of the widely used maxP technique is lacking. The optimal search methods require large computational time. We report the theoretical analysis and experimental results of the strengths and limitations of the maxP technique. Further, using an optimal search method, we combine the strengths of the maxP technique and the known GRN topology to propose two novel algorithms. These algorithms are implemented in a Bayesian network framework and tested on biological, realistic, and in silico networks of different sizes and topologies. They overcome the limitations of the maxP technique and show superior computational speed when compared to the current optimal search algorithms.

DFS based partial pathways in GA for protein structure prediction

- Hoque, Md Tamjidul, Chetty, Madhu, Lewis, Andrew, Sattar, Abdul

  • Authors: Hoque, Md Tamjidul , Chetty, Madhu , Lewis, Andrew , Sattar, Abdul
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Third IAPR International Conference, PRIB 2008
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  • Description: Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation being compact can produce many invalid conformations (by having non-self-avoiding-walk) using crossover. While a crossover-based converging conformation suffers from limited pathways, combining it with depth-first search (DFS) can partially reveal potential pathways. DFS generates random conformations increasingly quickly with increasing length of the protein sequences compared to random-move-only-based conformation generation. Random conformations are frequently applied for maintaining diversity as well as for initialization in many GA variations.

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

- Morshed, Nizamul, Chetty, Madhu, Nguyen, Vinh

  • Authors: Morshed, Nizamul , Chetty, Madhu , Nguyen, Vinh
  • Date: 2012
  • Type: Text , Journal article
  • Relation: BMC systems biology Vol. 6, no. 62 (2012), p. 62
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  • Description: Background: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.

An improved method to infer gene regulatory network using s-system

- Chowdhury, Ahsan, Chetty, Madhu

  • Authors: Chowdhury, Ahsan , Chetty, Madhu
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: IEEE Congress on Evolutionary Computation (IEEE CEC) p. 1012-1019
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  • Description: Abstract—Gene Regulatory Network (GRN) plays an important role in the understanding of complex biological systems. In most cases, high throughput microarray gene expression data is used for finding these regulatory relationships among genes. In this paper, we present a novel approach, based on decoupled SSystem model, for reverse engineering GRNs. In the proposed method, the genetic algorithm used for scoring the networks contains several useful features for accurate network inference, namely a Prediction Initialization (PI) algorithm to initialize the individuals, a Flip Operation (FO) for better mating of values and a restricted execution of Hill Climbing Local Search over few individuals. It also includes a novel refinement technique which utilizes the fit solutions of the genetic algorithm for optimizing sensitivity and specificity of the inferred network. Comparative studies and robustness analysis using standard benchmark data set show the superiority of the proposed method.

Extended HP model for protein structure prediction

- Hoque, Md Tamjidul, Chetty, Madhu, Sattar, Abdul

  • Authors: Hoque, Md Tamjidul , Chetty, Madhu , Sattar, Abdul
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Computational Biology and Bioinformatics Vol. Jan-Feb 2011, no. (2009 ), p. 234-245
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  • Description: This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.

Multiclass microarray gene expression classification based on fusion of correlation features

- Chetty, Girija, Chetty, Madhu

  • Authors: Chetty, Girija , Chetty, Madhu
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Information Fusion (FUSION), 2010 13th Conference
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  • Description: In this paper, we propose novel algorithmic models based on fusion of independent and correlated gene features for multiclass microarray gene expression classification. It is possible for genes to get co-expressed via different pathways. Moreover, a gene may or may not be co-active for all samples. In this paper, we approach this problem with a optimal feature selection technique using analysis based on statistical techniques to model the complex interactions between genes. The two different types of correlation modelling techniques based on the cross modal factor analysis (CFA) and canonical correlation analysis (CCA) were examined. The subsequent fusion of CCA/CFA features with principal component analysis (PCA) features at feature-level, and at score-level result in significant enhancement in classification accuracy for different data sets corresponding to multiclass microarray gene expression data.

Multiclass microarray gene expression analysis based on mutual dependency models

- Chetty, Girija, Chetty, Madhu

  • Authors: Chetty, Girija , Chetty, Madhu
  • Date: 2009
  • Type: Text , Book chapter
  • Relation: 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. p. 46-55
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  • Description: In this paper a novel feature selection technique based on mutual dependency modelling between genes is proposed for multiclass microarray gene expression classification. Several studies on analysis of gene expression data has shown that the genes (whether or not they belong to the same gene group) get co-expressed via a variety of pathways. Further, a gene may participate in multiple pathways that may or may not be co-active for all samples. It is therefore biologically meaningful to simultaneously divide genes into functional groups and samples into co-active categories. This can be done by modeling gene profiles for multiclass microarray gene data sets based on mutual dependency models, which model complex gene interactions. Most of the current works in multiclass microarray gene expression studies are based on statistical models with little or no consideration of gene interactions. This has led to lack of robustness and overly optimistic estimates of accuracy and noise reduction. In this paper, we propose multivariate analysis techniques which model the mutual dependency between the features and take into account complex interactions for extracting a subset of genes. The two techniques, the cross modal factor analysis (CFA) and canonical correlation analysis(CCA) show a significant reduction in dimensionality and class-prediction error, and improvement in classification accuracy for multiclass microarray gene expression datasets.

Issues impacting genetic network reverse engineering algorithm validation using small networks

- Nguyen, Vinh, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod

  • Authors: Nguyen, Vinh , Chetty, Madhu , Coppel, Ross , Wangikar, Pramod
  • Date: 2012
  • Type: Text , Journal article
  • Relation: BBA - Proteins and Proteomics Vol. 1824, no. 12 (2012), p. 1434-1441
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  • Description: Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth. In this study, we highlight a major issue that the validation of reverse engineering algorithms on small benchmark networks very often results in networks which are not statistically better than a randomly picked network. Another important issue highlighted is that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, we have selected as our case study the IRMA in-vivo synthetic yeast network recently published in Cell. Using Fisher's exact test, we show that many results reported in the literature on reverse-engineering this network are not significantly better than random. The discussion is further extended to some other networks commonly used for validation purposes in the literature. The results presented in this study emphasize that studies carried out using small genetic networks are likely to be trivial, making it imperative that larger real networks be used for validating and benchmarking purposes. If smaller networks are considered, then the results should be interpreted carefully to avoid over confidence. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Information theoretic dynamic Bayesian network approach for reconstructing genetic networks

- Morshed, Nizamul, Chetty, Madhu

  • Authors: Morshed, Nizamul , Chetty, Madhu
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: Proceedings of the Eleventh IASTED International Conference on Artificial Intelligence and Applications p. 236-243
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  • Description: A holistic understanding of genetic interactions, in the post-genomic era, is vital for analysing complex biological systems. In this paper, we present an information theory based novel gene regulatory network inference method using the dynamic Bayesian network (DBN) framework. The proposed approach, with strong theoretical underpinnings, employs mutual information based conditional independence tests to assess the regulatory relationships among genes. The method is flexible, computationally fast and allows a-priori incorporation of biological domain knowledge. We apply it to the analysis of synthetic data as well as Saccharomyces cerevisiae (yeast cell cycle) gene expression data. Performance measures applied to simulation studies show the superior performance of the proposed method

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

- Morshed, Nizamul, Chetty, Madhu, Xuan Vinh, Nguyen

  • Authors: Morshed, Nizamul , Chetty, Madhu , Xuan Vinh, Nguyen
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: International Conference on Neural Information Processing p. 248-257
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  • Description: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations assumes that genes interact either instantaneously or with time delay. In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. Also, a novel scoring metric having firm mathematical underpinnings is then proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the biological fact that multiple regulators may regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network inference method employing evolutionary search that makes use of the framework and the scoring metric is also presented. Experiments carried out using synthetic data as well as the well known Saccharomyces cerevisiae gene expression data show the effectiveness of our approach.

A knowledge-based initial population generation in memetic algorithm for protein structure prediction

- Nazmul, Rumana, Chetty, Madhu

  • Authors: Nazmul, Rumana , Chetty, Madhu
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: 20th International Conference, ICONIP 2013 p. 546-553
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  • Description: Predicting the minimum energy protein structure from its amino acid sequence, even under the rather simplified HP lattice model, continues to be an important and challenging problem in computational biology. In this paper, we propose a novel initial population generation strategy for evolutionary algorithm incorporating domain knowledge based on the concept of maximum hydrophobic core formation for Protein structure prediction (PSP) problem. The proposed technique helps the optimization process to commence with diverse seeds and thereby aids in converging to the global solution quickly. The experimental results, conducted on PSP problem using HP benchmark sequences for 2D square and 3D cubic lattice model, demonstrate that the proposed evolutionary algorithm with new core-based population initialization technique is very effective in improving the optimization process in terms of convergence as well as in achieving the optimal energy.

Reconstruction of Large-scale gene regulatory network using s-system model

- Chowdhury, Ahsan, Chetty, Madhu

Inferring large scale genetic networks with S-System model

- Chowdhury, Ahsan, Chetty, Madhu, Nguyen, Vinh

A study on the importance of differential prioritization in feature selection using toy datasets

- Ooi, Chia, Teng, Shyh, Chetty, Madhu

  • Authors: Ooi, Chia , Teng, Shyh , Chetty, Madhu
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Third IAPR International Conference, PRIB
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  • Description: Previous empirical works have shown the effectiveness of differential prioritization in feature selection prior to molecular classification. We now propose to determine the theoretical basis for the concept of differential prioritization through mathematical analyses of the characteristics of predictor sets found using different values of the DDP (degree of differential prioritization) from realistic toy datasets. Mathematical analyses based on analytical measures such as distance between classes are implemented on these predictor sets. We demonstrate that the optimal value of the DDP is capable of forming a predictor set which consists of classes of features which are well separated and are highly correlated to the target classes – a characteristic of a truly optimal predictor set. From these analyses, the necessity of adjusting the DDP based on the dataset of interest is confirmed in a mathematical manner, indicating that the DDP-based feature selection technique is superior to both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods. Applying similar analyses to real-life multiclass microarray datasets, we obtain further proof of the theoretical significance of the DDP for practical applications

Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique

- Morshed, Nizamul, Chetty, Madhu, Nguyen, Vinh

  • Authors: Morshed, Nizamul , Chetty, Madhu , Nguyen, Vinh
  • Date: 2012
  • Type: Text , Journal article
  • Relation: BMC Systems Biology Vol. 6, no. 1 (2012), p. 62-76
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  • Description: Background: Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations using the Bayesian Network (BN) formalism assumes that genes interact either instantaneously or with a certain amount of time delay. However in reality, biological regulations, both instantaneous and time-delayed, occur simultaneously. A framework that can detect and model both these two types of interactions simultaneously would represent gene regulatory networks more accurately. Results: In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. A novel scoring metric having firm mathematical underpinnings is also proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the reality that multiple regulators can regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented. Conclusion: By taking into consideration the biological fact that both instantaneous and time-delayed regulations can occur among genes, our approach models gene interactions with greater accuracy. The proposed framework is efficient and can be used to infer gene networks having multiple orders of instantaneous and time-delayed regulations simultaneously. Experiments are carried out using three different synthetic networks (with three different mechanisms for generating synthetic data) as well as real life networks of Saccharomyces cerevisiae, E. coli and cyanobacteria gene expression data. The results show the effectiveness of our approach.

Network decomposition based large-scale reverse engineering of gene regulatory network

- Chowdhury, Ahsan, Chetty, Madhu

  • Authors: Chowdhury, Ahsan , Chetty, Madhu
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 160, no. (2015), p. 213-227
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  • Description: A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the regulatory relationships among genes of a cellular system at the gene level. In real-life biological networks, the number of genes present are very large exhibiting both, the instantaneous and time-delayed regulations. While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains. In this paper, we propose a novel methodology for large-scale modeling of GRNs by decomposing the GRN into two independent sub-networks utilizing its biological traits. Using the time-delayed S-system model [1], these two sub-networks are learnt separately and then combined to get the entire GRN. To speed up the inference mechanism, a cardinality-based fitness function, especially developed for inferring large-scale GRNs is proposed to allow incorporation of knowledge of maximum in-degree. A novel local-search method is also proposed to further facilitate the incorporation of biological knowledge by gene clustering and gene ranking. Experimental studies demonstrate that the proposed approach is successful in learning large genetic networks, currently not achievable with existing S-system based modeling approaches.

mDBN: motif based learning of gene regulatory networks using dynamic Bayesian networks

- Morshed, Nizamul, Chetty, Madhu, Nguyen, Vinh, Caelli, Terry

Influence of mixotrophic growth on rhythmic oscillations in expression of metabolic pathways in diazotrophic cyanobacterium Cyanothece sp ATCC 51142

- Krishnakumar, Sujatha, Gaudana, Sandeep, Digmurti, Madhuri, Viswanathan, Ganesh, Chetty, Madhu, Wangikar, Pramod

  • Authors: Krishnakumar, Sujatha , Gaudana, Sandeep , Digmurti, Madhuri , Viswanathan, Ganesh , Chetty, Madhu , Wangikar, Pramod
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Bioresource Technology Vol. 188, no. (2015), p. 145-152
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  • Description: This study investigates the influence of mixotrophy on physiology and metabolism by analysis of global gene expression in unicellular diazotrophic cyanobacterium Cyanothece sp. ATCC 51142 ( henceforth Cyanothece 51142). It was found that Cyanothece 51142 continues to oscillate between photosynthesis and respiration in continuous light under mixotrophy with cycle time of similar to 13 h. Mixotrophy is marked by an extended respiratory phase compared with photoautotrophy. It can be argued that glycerol provides supplementary energy for nitrogen fixation, which is derived primarily from the glycogen reserves during photoautotrophy. The genes of NDH complex, cytochrome c oxidase and ATP synthase are significantly overexpressed in mixotrophy during the day compared to autotrophy with synchronous expression of the bidirectional hydrogenase genes possibly to maintain redox balance. However, nitrogenase complex remains exclusive to nighttime metabolism concomitantly with uptake hydrogenase. This study throws light on interrelations between metabolic pathways with implications in design of hydrogen producer strains. (C) 2015 Elsevier Ltd. All rights reserved.