EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover

In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably the first one capable of discovering with accuracy exceeding...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Orzechowski, Patryk, Sipper, Moshe, Huang, Xiuzhen, Moore, Jason H
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Datasets
Evolutionary algorithms
Gene expression
Graphics processing units
Machine learning
Online Access:Get full text
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Summary:In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably the first one capable of discovering with accuracy exceeding 50% multiple complex patterns in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. The proposed algorithm is anticipated to be added to the repertoire of unsupervised machine learning algorithms for the analysis of datasets, including those from large-scale genomic studies.
ISSN:2331-8422
DOI:10.48550/arxiv.1801.03039