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View all- Ahmed SRoy S(2020)Inference and analysis of gene regulatory networks using data mining techniquesACM SIGBioinformatics Record10.1145/3383672.338367510:1(1-2)Online publication date: 13-Feb-2020
Information Theoretic Approaches for Detecting Causality in Gene Regulatory Networks
Authors: 
Kangkan Medhi, Syed Sazzad Ahmed, 
Swarup Roy, Dhruba K. Bhattacharyya, Jugal K. KalitaAuthors Info & Claims
Kangkan Medhi, Syed Sazzad Ahmed, Swarup Roy, Dhruba K. Bhattacharyya, Jugal K. KalitaAuthors Info & ClaimsArticle No.: 128, Pages 1 - 5
Abstract
Causality detection in gene regulatory networks (GRN) is a challenging and important task. Very few techniques have been proposed so far to infer causality in GRN. A majority of them adapts information theory as a measure to infer a causal relationship. In this work we evaluate the performance of information theoretic causality detection techniques in GRN.
We consider two such measures, namely, Transfer Entropy and Interaction Information and compare their performance with Granger causality, a statistical causality inference method. For evaluation, we use synthetic gold standard data and underlying causal networks from DREAM challenges.
Experimental results reveal that Interaction Information performs better in comparison to other candidate methods for inferring causality in GRN. It is also evident from the results that performance of information theoretic approaches is sensitive towards discretization method used.
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Published In
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
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Association for Computing Machinery
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Published: 04 March 2016
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ICTCS '16
ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies
March 4 - 5, 2016
Udaipur, India
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Overall Acceptance Rate 97 of 270 submissions, 36%
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View all- Ahmed SRoy S(2020)Inference and analysis of gene regulatory networks using data mining techniquesACM SIGBioinformatics Record10.1145/3383672.338367510:1(1-2)Online publication date: 13-Feb-2020
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