EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

ABSTRACT:

Motivation

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance.

Results

In this paper, a novel biclustering algorithm based on evolutionary computation, a sub-field of artificial intelligence, is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units. We demonstrate that E

SUBMITTER: Orzechowski P 

PROVIDER: S-EPMC6198864 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature