Cited By
View all- Medvet ENadizar GManzoni LWagner M(2022)JGEAProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533960(2009-2018)Online publication date: 9-Jul-2022
EBIC.JL: an efficient implementation of evolutionary biclustering algorithm in Julia
Authors: 
Paweł Renc, Patryk Orzechowski, Aleksander Byrski, Jarosław Wăs, Jason H. MooreAuthors Info & Claims
Pages 1540 - 1548
Abstract
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multiple areas, including development of biomarkers for cancer, disease subtype identification, or gene-drug interactions among others. In this paper we introduce EBIC.JL - an implementation of one of the most accurate biclustering algorithms in Julia, a modern highly parallelizable programming language for data science. We show that the new version maintains comparable accuracy to its predecessor EBIC while converging faster for the majority of the problems. We hope that this open source software in a high-level programming language will foster research in this promising field of bioinformatics and expedite development of new biclustering methods for big data.
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View all- Medvet ENadizar GManzoni LWagner M(2022)JGEAProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3533960(2009-2018)Online publication date: 9-Jul-2022
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