EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia

TL;DR

This paper introduces EBIC.JL, a Julia implementation of a highly accurate biclustering algorithm that is faster and suitable for big data applications in bioinformatics.

Contribution

The paper presents a new Julia-based implementation of EBIC that maintains accuracy while improving convergence speed, facilitating research in bioinformatics.

Findings

Maintains comparable accuracy to original EBIC

Converges faster on most problems

Open source implementation in Julia

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.