Entropic Biological Score: a cell cycle investigation for GRNs inference

Autor(es)

Lopes, Fabricio M. ^[1] ; Ray, Shubhra Sankar ^{[2, 3]} ; Hashimoto, Ronaldo F. ^[4] ; Cesar, Jr., Roberto M. ^[4]

Afiliação do(s) autor(es)

^[1] Fed Univ Technol, Curitiba, Parana - Brazil

^[2] Indian Stat Inst, Ctr Soft Comp Res, Kolkata 700108 - India

^[3] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108 - India

^[4] Univ Sao Paulo, Inst Math & Stat, BR-05508 Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Gene; v. 541, n. 2, p. 129-137, MAY 15 2014.
• Citações Web of Science: 12

Resumo

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 w(1) = 0.2 and w(2) = 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. (C) 2014 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 10/52138-8 - Integração de dados na biologia sistêmica: caracterização de fenômenos biológicos a partir de informações estruturais e funcionais
• Beneficiário: Ronaldo Fumio Hashimoto
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
• Beneficiário: Roberto Marcondes Cesar Junior
• Linha de fomento: Auxílio à Pesquisa - Temático