Research paper presenting an imbalanced dataset for machine learning-based quality control of next-generation sequencing. Addresses the technical challenge of applying ML to NGS data with class imbalance, a common problem in genomics workflows.
Research
An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing
New imbalanced dataset enables ML-based quality control for genomics sequencing workflows, addressing a persistent class imbalance problem that blocks production automation of NGS pipelines.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
Tags
research