mDBN: motif based learning of gene regulatory networks using dynamic Bayesian networks
- Authors: Morshed, Nizamul , Chetty, Madhu , Nguyen, Vinh , Caelli, Terry
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO'13, Association for Computing Machinery Inc. (ACM), 2013 p. 279-286
- Full Text: false
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- Description: Solutions for deriving the most consistent Bayesian gene regulatory network model from given data sets using evolutionary algorithms typically only result in locally optimal solutions.
Reconstructing genetic networks with concurrent representation of instantaneous and time-delayed interactions
- Authors: Morshed, Nizamul , Chetty, Madhu
- Date: 2011
- Type: Text , Conference paper
- Relation: IEEE Congress on Evolutionary Computation (IEEE CEC) p. 1840-1847
- Full Text: false
- Reviewed:
- Description: Although living organisms can have some genetic interactions occurring instantaneously while others with time-delay, current modeling techniques for genetic network reconstruction make simplifications and assume that the interactions can be either of these but not both. In this paper, we propose a gene regulatory network reconstruction algorithm that can model concurrent occurrence of both, instantaneous as well as time-delayed interactions, thus providing a better representation of the original biological processes. First we introduce a novel framework using the Bayesian network (BN) formalism that can model both types of interactions. A gene regulatory network reconstruction algorithm using this proposed framework is then developed that employs an evolutionary search strategy and a decomposable scoring metric based on information theoretic quantities. Investigations of our approach are performed using both, the synthetic data as well as Saccharomyces cerevisiae gene expression data. Comparisons with recent reconstruction methods show the superiority of the proposed method.
Information theoretic dynamic Bayesian network approach for reconstructing genetic networks
- 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
- Full Text: false
- Reviewed:
- 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
FusGP: Bayesian co-learning of gene regulatory networks and protein interaction networks
- Authors: Morshed, Nizamul , Chetty, Madhu , Nguyen, Vinh
- Date: 2012
- Type: Text , Conference paper
- Relation: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012; published in Neural Information Processing, Part V (Lecture Notesin Computer Science series) Vol.7667 p. 369-377
- Full Text: false
- Reviewed:
- Description: Understanding gene interactions is a fundamental question in uncovering the underlying biological relations that enable successful functioning of living organisms. The modeling of gene regulations is usually done using DNA microarray data. However, presence of noise and the scarcity of microarray data affect the reconstruction of gene regulatory networks. In this paper, we propose a novel co-learning based fusion algorithm using the dynamic Bayesian netowrk (DBN) formalism for reconstruction of gene regulatory networks which incorporates knowledge obtained from protein-protein interaction networks to improve network accuracy. The proposed approach is efficient and naturally amenable to parallel computation. We apply the algorithm on the well-known Saccharomyces cerevisiae gene expression data that shows the effectiveness of our approach.
Combining instantaneous and time-delayed interactions between genes - a two phase algorithm based on information theory
- Authors: Morshed, Nizamul , Chetty, Madhu
- Date: 2011
- Type: Text , Conference paper
- Relation: AI'11 Perth, Australia, Dec 5th-8th, 2011; published in Proceedings of the 24th international conference on Advances in Artificial Intelligence, (LNCS: Lecture Notes In Computer Science) vol 7106 p.102-111
- Full Text: false
- Reviewed:
- Description: Understanding the way how genes interact is one of the fundamental questions in systems biology. The modeling of gene regulations currently assumes that genes interact either instantaneously or with a certain amount of time delay. In this paper, we propose an information theory based novel two-phase gene regulatory network (GRN) inference algorithm using the Bayesian network formalism that can model both instantaneous and single-step time-delayed interactions between genes simultaneously. We show the effectiveness of our approach by applying it to the analysis of synthetic data as well as the Saccharomyces cerevisiae gene expression data.
Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique
- Authors: Morshed, Nizamul , Chetty, Madhu , Xuan Vinh, Nguyen
- Date: 2011
- Type: Text , Conference paper
- Relation: International Conference on Neural Information Processing p. 248-257
- Full Text: false
- Reviewed:
- 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.
Simultaneous learning of instantaneous and time-delayed genetic interactions using novel information theoretic scoring technique
- 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.