Analyzing Prognosis Characteristics of Hepatitis C using a Biclustering Based Approach

被引：4

作者：

Hossain, Sk Md Mosaddek ^{[1
]}

Ray, Sumanta ^{[1
]}

Tannee, Tabassum Sultana ^{[1
]}

Mukhopadhyay, Anirban ^{[2
]}

机构：

[1] Aliah Univ, Dept Comp Sci & Engn, Kolkata 700156, W Bengal, India

[2] Univ Kalyani, Dept Comp Sci & Engn, Kalyani 741235, W Bengal, India

来源：

7TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2017) | 2017年 / 115卷

关键词：

microarray gene expression data; biclustering; principal component analysis; gene ontology; pathways; EXPRESSION; ALGORITHMS;

D O I：

10.1016/j.procs.2017.09.136

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

DNA microarray technology has led to huge advancement in the diagnosis process of diseases. In this paper, we have developed a framework to analyze gene expression data of three different Hepatitis C related prognosis datasets. First, we have discovered differentially expressed genes for each pair of prognosis datasets. Principal Component Analysis has been carried out for reduction of samples size and computational convenience. The difference in local expression patterns between each pair of prognosis is identified through similarity scores among biclusters. A gene ontology and pathway based analysis has been performed for understanding biological significance of the identified biclusters. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 7th International Conference on Advances in Computing & Communications.

引用

页码：282 / 289

页数：8

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