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.

中文翻译：

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EBIC.JL-朱莉娅（Eric Julia）中进化双聚类算法的有效实现

Biclustering是一种数据挖掘技术，主要在生物信息学中搜索数字表格数据中的局部模式。这项技术在多个领域都显示出了希望，包括开发用于癌症，疾病亚型鉴定或基因药物相互作用的生物标志物。在本文中，我们介绍EBIC.JL-朱莉娅（Julia）中一种最精确的双聚类算法的实现，朱莉娅是一种现代的高度可并行化的数据科学编程语言。我们证明，新版本在保留大多数问题的同时，可以保持与之前的EBIC相当的准确性。我们希望这种以高级编程语言开发的开源软件将促进这一有前途的生物信息学领域的研究，并加快新的大数据双聚类方法的开发。