{"ID":2838417,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16923","arxiv_id":"2511.16923","title":"A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests","abstract":"Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable performance comparable to or exceeding existing imputation methods in most cases, while preserving biological fidelity and transparency. Runtime analysis demonstrates that SCR-MF provides a competitive balance between accuracy and computational efficiency, making it suitable for mid-scale single-cell datasets.","short_abstract":"Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via m...","url_abs":"https://arxiv.org/abs/2511.16923","url_pdf":"https://arxiv.org/pdf/2511.16923v1","authors":"[\"Ali Anaissi\",\"Deshao Liu\",\"Yuanzhe Jia\",\"Weidong Huang\",\"Widad Alyassine\",\"Junaid Akram\"]","published":"2025-11-21T03:23:47Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.GN\"]","methods":"[]","has_code":false}