GENBoostGPU: Genomic Elastic Net Boosting on GPU

GENBoostGPU provides a scalable framework for running elastic net regression with boosting across thousands of CpG sites or regions, leveraging GPU acceleration.

It supports SNP preprocessing, cis-window filtering, LD clumping, missing data imputation, and phenotype integration — all optimized for large-scale epigenomics.

PyPI: https://pypi.org/project/genboostgpu/

RFMix-reader: Accelerated reading and processing for local ancestry studies

Local ancestry inference is crucial for understanding population history and disease genetics, especially for eQTL studies in admixed populations. While RFMix is widely used, handling its output for large datasets is challenging due to high memory and processing demands. To address this, RFMix-reader efficiently processes large local ancestry datasets, leveraging GPUs for speed and minimizing memory usage, enabling deeper insights into human health and health disparities.

PyPI: https://pypi.org/project/rfmix-reader/

Documentation: http://rfmix-reader.readthedocs.io/

rfmix-reader

dRFEtools: dynamic recursive feature elimination for omics

Technology advances have generated larger ‘OMICs datasets with applications for machine learning. Even so, sample availability results in smaller sample sizes compared to features. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to tackle this problem. dRFEtools provides an interpretable and flexible tool to gain biological insights from ‘OMICs data using machine learning.

PyPI: https://pypi.org/project/drfetools/

Documentation: http://drfetools.readthedocs.io/

dRFEtools overview