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In this paper a novel biclustering algorithm based on artificial intelligence(AI) is introduced. The method called EBIC aims to detect biologicallymeaningful, order-preserving patterns in complex data. The proposed algorithmis probably the first one capable of discovering with accuracy exceeding 50%multiple complex patterns in real gene expression datasets. It is also one ofthe very few biclustering methods designed for parallel environments withmultiple graphics processing units (GPUs). We demonstrate that EBIC outperformsstate-of-the-art biclustering methods, in terms of recovery and relevance, onboth synthetic and genetic datasets. EBIC also yields results over 12 timesfaster than the most accurate reference algorithms. The proposed algorithm isanticipated to be added to the repertoire of unsupervised machine learningalgorithms for the analysis of datasets, including those from large-scalegenomic studies.

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