Bicluster Detection by Hyperplane Projection and Evolutionary Optimization

Maryam Golchin, Alan Wee-Chung Liew

2018

Abstract

Biclustering is a powerful unsupervised learning technique that has different applications in many fields especially in gene expression analysis. This technique tries to group rows and columns in a dataset simultaneously, which is an NP-hard problem. In this paper, a multi-objective evolutionary algorithm is proposed with a heuristic search to solve the biclustering problem. To do so, rows are projected into the column space. Projection decreases the computational cost of geometric biclustering. The heuristic search is done by sample Pearson correlation coefficient over the rows and columns of a dataset to prune unwanted rows and columns. The experimental results on both synthetic and real datasets show the effectiveness of our proposed method.

Paper Citation

in Harvard Style

Golchin M. and Liew A. (2018). Bicluster Detection by Hyperplane Projection and Evolutionary Optimization. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-280-6, SciTePress, pages 61-68. DOI: 10.5220/0006710000610068

in Bibtex Style

@conference{bioinformatics18,
author={Maryam Golchin and Alan Wee-Chung Liew},
title={Bicluster Detection by Hyperplane Projection and Evolutionary Optimization},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 3: BIOINFORMATICS},
year={2018},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006710000610068},
isbn={978-989-758-280-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 3: BIOINFORMATICS
TI - Bicluster Detection by Hyperplane Projection and Evolutionary Optimization
SN - 978-989-758-280-6
AU - Golchin M.
AU - Liew A.
PY - 2018
SP - 61
EP - 68
DO - 10.5220/0006710000610068
PB - SciTePress