genboostgpu 0.1.1

GENBoostGPU provides a scalable framework for running elastic net regression with boosting across thousands of CpG sites, leveraging GPU acceleration with RAPIDS cuML, CuPy, and cuDF.

GENBoostGPU

Genomic Elastic Net Boosting on GPU (GENBoostGPU)

GENBoostGPU provides a scalable framework for running elastic net regression with boosting across thousands of CpG sites, leveraging GPU acceleration with RAPIDS cuML, CuPy, and cuDF. It supports SNP preprocessing, cis-window filtering, LD clumping, missing data imputation, and phenotype integration — all optimized for large-scale epigenomics.

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Features

• GPU-accelerated elastic net regression with optional boosting
• SNP-level preprocessing:
  • Zero-variance SNP filtering
  • Missing genotype imputation
  • LD clumping (PLINK-like) on GPU
• Cis-window filtering for CpGs
• Integration of genotype (PLINK) and phenotype (CpG/VMR methylation) data
• Batch execution across thousands of CpGs on a single GPU
• Flexible output: betas, heritability estimates, cross-validation results

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