Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

Full text
Author(s):
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]
Total Authors: 8
Affiliation:
[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
Total Affiliations: 7
Document type: Journal article
Source: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS; v. 4, n. 1 DEC 2015.
Web of Science Citations: 1
Abstract

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)

FAPESP's process: 10/52138-8 - Data integration in systems biology: characterization of biological phenomena from structural and functional information
Grantee:Ronaldo Fumio Hashimoto
Support type: Regular Research Grants
FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
Grantee:Roberto Marcondes Cesar Junior
Support type: Research Projects - Thematic Grants