Towards the genome-scale discovery of bivariate monotonic classifiers

We provide the first open-source implementation for learning BMCs, a Python implementation of fastBMC in particular, and Python code to reproduce the fastBMC results on real and simulated data in this paper, at https://github.com/oceanefrqt/fastBMC .

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Publication Details

Journal
BMC Bioinformatics
Published
2025-09-02
DOI
https://doi.org/10.1186/s12859-025-06253-7
Primary Topic
Gene expression and cancer classification
Type
article
Field-Weighted Citation Impact
0.00

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article

Towards the genome-scale discovery of bivariate monotonic classifiers

Carola Doerr, Martin S. Krejca, Benno Schwikowski, Océane Fourquet
BMC Bioinformatics
Gene expression and cancer classification
article

Towards the genome-scale discovery of bivariate monotonic classifiers

Carola Doerr, Martin S. Krejca, Benno Schwikowski, Océane Fourquet
article en

Abstract

We provide the first open-source implementation for learning BMCs, a Python implementation of fastBMC in particular, and Python code to reproduce the fastBMC results on real and simulated data in this paper, at https://github.com/oceanefrqt/fastBMC .

BMC BioinformaticsVol. 26(1)
Centre National de la Recherche Scientifique (FR), École Polytechnique (FR), Institut Pasteur (FR), Université Paris Cité (FR), Sorbonne Université (FR)
Horizon 2020 Framework Programme
Openalex Percentile: Top 79%
Gene expression and cancer classification
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Towards the genome-scale discovery of bivariate monotonic classifiers — Carola Doerr, Martin S. Krejca, et al. · BMC Bioinformatics (2025) | TGRS Research Map | TGRS