# A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests

arXiv: https://arxiv.org/abs/2511.16923 · HTML: https://mlsanity.com/paper/2511.16923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels](https://mlsanity.com/paper/2610.13221/) (2026)

## Abstract (arXiv)

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.