Network topology measures for identifying disease-gene association in breast cancer. Issue 7 (July 2016)

• Record Type: Journal Article
• Authors: Ramadan, Emad; Alinsaif, Sadiq; Hassan, Md
• Is Part Of: BMC bioinformatics. Volume 17:Issue 7(2016)
• Page Start: 473
• Page End: 480
• Publication Date: 2016-07
• Subjects: Biological networks -- Machine learning -- Phenotype-gene association; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1095-5
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Massive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and pharmacists. Recently, analysis of biological networks has received a lot of attention, as an understanding of the network can reveal information about life at the cellular level. Biological networks can be generated that examine the interaction between proteins or the relationship amongst different genes at the expression level. Identifying information from biological networks is recognized as a significant challenge, due to the inherent complexity of the structures. Computational techniques are used to analyze such complex networks with varying success. Results In this paper, we construct a new method for predicting phenotype-gene association in breast cancer using biological network analysis. Several network topological measures have been computed and fed as features into two classification models to investigate phenotype-gene association in breast cancer. More importantly, to overcome the problem of the skewed datasets, a synthetic minority oversampling technique (SMOTE) is adapted in order to transform an imbalanced dataset to a balanced one. We have applied our method on the gene co-expression network (GCN), protein–protein interaction network (PPI), and the integrated functional interaction network (FI), which combined the PPIs and gene co-expression, amongst others. We assess the quality of our proposed method using a slightly modified cross-validation. Conclusions Our method can identify phenotype-gene association in breast cancer. Moreover, use of the integrated functional interaction network (FI) has the potential to reveal more information and hidden patterns than the other networks. The software and accompanying examples are freely available athttp://faculty.kfupm.edu.sa/ics/eramadan/NetTop.zip .