Bicluster detection by hyperplane projection and evolutionary optimization

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Golchin, M
Wee-Chung Liew, A
Griffith University Author(s)
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2018
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Funchal, Portugal

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Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 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.

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BIOINFORMATICS 2018 - 9th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

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3

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© 2018 ScitePress. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.

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Pattern recognition

Semi- and unsupervised learning

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