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

arXiv: https://arxiv.org/abs/2105.01196 · HTML: https://mlsanity.com/paper/2105.01196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 10 of 10)

- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Fashionable Modelling with Flux](https://mlsanity.com/paper/1811.01457/) (2018)
- [Where are we now? A large benchmark study of recent symbolic regression methods](https://mlsanity.com/paper/1804.09331/) (2018)
- [Epsilon-Lexicase Selection for Regression](https://mlsanity.com/paper/1905.13266/) (2019)
- [Automating biomedical data science through tree-based pipeline optimization](https://mlsanity.com/paper/1601.07925/) (2016)
- [Effective Extensible Programming: Unleashing Julia on GPUs](https://mlsanity.com/paper/1712.03112/) (2017)
- [MLJ: A Julia package for composable machine learning](https://mlsanity.com/paper/2007.12285/) (2020)
- [Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations](https://mlsanity.com/paper/1712.05043/) (2017)
- [EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover](https://mlsanity.com/paper/1801.03039/) (2018)
- [EBIC: an open source software for high-dimensional and big data biclustering analyses](https://mlsanity.com/paper/1807.09932/) (2018)

## Abstract (arXiv)

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.