By Topic

A novel computational framework for fast distributed computing and knowledge integration for microarray gene expression data analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Sethi, P. ; Comput. Sci. Program, Louisiana Tech. Univ., Ruston, LA, USA ; Leangsuksun, C.B.

Rapid technological advancements in microarray analysis continue to generate enormous amounts of genomic data. However, neither hardware nor software computational capabilities have kept pace with this drastic increase. This paper presents a novel framework designed to achieve fast, robust, and accurate (biologically-significant) multi-class classification of gene expression data using distributed knowledge discovery and computational integration routines, specifically for cancer applications. The proposed paradigm consists of the following key computational steps: (a) preprocessing normalization and discretization of gene expression data, (b) partition data using two methods: overlapped windows and adaptive selection, (c) perform association rule discovery on partitioned data-spaces using FP-growth method, (d) integrate derived association rules on distributed processor nodes using a novel knowledge integration algorithm, (e) further prune rules to reduce dimensionality using parametric significance estimation, and (f) cluster remaining rules using a novel clustering algorithm for enhanced visualization and interpretation of discovered gene rule sets.

Published in:

Advanced Information Networking and Applications, 2006. AINA 2006. 20th International Conference on  (Volume:2 )

Date of Conference:

18-20 April 2006