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Authors: Jeffrey Dale ; America Nishimoto and Tayo Obafemi-Ajayi

Affiliation: Missouri State University, United States

Keyword(s): Biclustering, Evaluation, Gene Expression Pattern Recognition, Validation Measures.

Related Ontology Subjects/Areas/Topics: Applications ; Bioinformatics and Systems Biology ; Clustering ; Pattern Recognition ; Software Engineering ; Theory and Methods

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.

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Paper citation in several formats:
Dale, J.; Nishimoto, A. and Obafemi-Ajayi, T. (2018). Performance Evaluation and Enhancement of Biclustering Algorithms. In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-276-9; ISSN 2184-4313, SciTePress, pages 202-213. DOI: 10.5220/0006662502020213

@conference{icpram18,
author={Jeffrey Dale. and America Nishimoto. and Tayo Obafemi{-}Ajayi.},
title={Performance Evaluation and Enhancement of Biclustering Algorithms},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2018},
pages={202-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006662502020213},
isbn={978-989-758-276-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Performance Evaluation and Enhancement of Biclustering Algorithms
SN - 978-989-758-276-9
IS - 2184-4313
AU - Dale, J.
AU - Nishimoto, A.
AU - Obafemi-Ajayi, T.
PY - 2018
SP - 202
EP - 213
DO - 10.5220/0006662502020213
PB - SciTePress

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