RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters | Zendy

Xiangyu Liu | Zendy; Di Li | Zendy; Juntao Liu | Zendy; Zhengchang Su | Zendy; Guojun Li | Zendy

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RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters

Author(s) -

Xiangyu Liu,

Di Li,

Juntao Liu,

Zhengchang Su,

Guojun Li

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa630

Subject(s) - computer science , biclustering , salient , robustness (evolution) , data mining , code (set theory) , source code , algorithm , pattern recognition (psychology) , artificial intelligence , cluster analysis , gene , biology , cure data clustering algorithm , biochemistry , correlation clustering , set (abstract data type) , programming language , operating system

Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets.

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