Entropic Biological Score: a cell cycle investigation for GRNs inference |
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Authors: | Fabrí cio M. Lopes,Shubhra Sankar Ray,Ronaldo F. Hashimoto,Roberto M. Cesar Jr. |
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Affiliation: | 1. Federal University of Technology, Paraná, Brazil;2. Center for Soft Computing Research and Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India;3. Institute of Mathematics and Statistics, University of São Paulo, Brazil;4. Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazil |
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Abstract: | Inference of gene regulatory networks (GRNs) is one of the most challenging research problems of Systems Biology. In this investigation, a new GRNs inference methodology, called Entropic Biological Score (EBS), which linearly combines the mean conditional entropy (MCE) from expression levels and a Biological Score (BS), obtained by integrating different biological data sources, is proposed. The EBS is validated with the Cell Cycle related functional annotation information, available from Munich Information Center for Protein Sequences (MIPS), and compared with some existing methods like MRNET, ARACNE, CLR and MCE for GRNs inference. For real networks, the performance of EBS, which uses the concept of integrating different data sources, is found to be superior to the aforementioned inference methods. The best results for EBS are obtained by considering the weights w1 = 0.2 and w2 = 0.8 for MCE and BS values, respectively, where approximately 40% of the inferred connections are found to be correct and significantly better than related methods. The results also indicate that expression profile is able to recover some true connections, that are not present in biological annotations, thus leading to the possibility of discovering new relations between its genes. |
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Keywords: | EBS, Entropic Biological Score BS, Biological Score MIPS, Munich Information Center for Protein Sequences GRNs, gene regulatory networks MCE, mean conditional entropy PPV, positive predictive value SFFS, Sequential Floating Forward Selection |
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