Performance Evaluation And Enhancement Of Biclustering Algorithms

Abstract

In gene expression data analysis, biclustering has proven to be an effective method of finding local patterns among subsets of genes and conditions. The task of evaluating the quality of a bicluster when ground truth is not known is challenging. In this analysis, we empirically evaluate and compare the performance of eight popular biclustering algorithms across 119 synthetic datasets that span a wide range of possible bicluster structures and patterns. We also present a method of enhancing performance (relevance score) of the biclustering algorithms to increase confidence in the significance of the biclusters returned based on four internal validation measures. The experimental results demonstrate that the Average Spearman's Rho evaluation measure is the most effective criteria to improve bicluster relevance with the proposed performance enhancement method, while maintaining a relatively low loss in recovery scores.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Biclustering; Evaluation; Gene Expression Pattern Recognition; Validation Measures

International Standard Book Number (ISBN)

978-989758276-9

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Scitepress; Science and Technology Publications, All rights reserved.

Publication Date

01 Jan 2018

Link to Full Text

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