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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Identifying dense subgraphs in protein-protein interaction network for gene selection from microarray data

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Autor(es):
Swarnkar, Tripti [1, 2] ; Simoes, Sergio Nery [3, 4] ; Anura, Anji [5] ; Brentani, Helena [6] ; Chatterjee, Jyotirmoy [5] ; Hashimoto, Ronaldo Fumio [3] ; Martins, David Correa [7] ; Mitra, Pabitra [1]
Número total de Autores: 8
Afiliação do(s) autor(es):
[1] Indian Inst Technol, Dept Comp Sci & Engn, Kharagpur 721302, W Bengal - India
[2] SOA Univ, Inst Tech Educ & Res, Bhubaneswar 751030 - India
[3] Univ Sao Paulo, Inst Math & Stat, Sao Paulo, SP - Brazil
[4] Fed Inst Espirito Santo, Serra, ES - Brazil
[5] Indian Inst Technol, Sch Med Sci & Technol, Kharagpur 721302, W Bengal - India
[6] Univ Sao Paulo, Inst Psychiat, Sao Paulo, SP - Brazil
[7] Fed Univ ABC, Ctr Math Comp & Cognit, Santo Andre, SP - Brazil
Número total de Afiliações: 7
Tipo de documento: Artigo Científico
Fonte: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS; v. 4, n. 1 DEC 2015.
Citações Web of Science: 1
Resumo

Selection of important genes responsible for a disease is an important task in bioinformatics. Microarray data are often used with differential expression being considered as a cue. Recently, such expression data are supplemented by gene ontology and genes/proteins interaction network for the selection task. The functional knowledge and interaction structure have become critical for understanding the biological processes, including selection of genes potentially associated to complex diseases. In this paper, we propose an approach that combines expression analysis with structural analysis of protein-protein interaction networks to identify genes associated with complex diseases. The dense subgraph structures embedded in the networks are extracted. We present results on three different types of benchmark cancer dataset (prostate cancer, interstitial lung disease and chronic lymphocytic leukemia) and show that several interesting biological information could be inferred, besides achieving a high prediction accuracy. The proposed methodology helps to identify not just differentially expressed genes but also hub genes important in biological processes. (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