The paper introduces SCR-MF, a two‑stage workflow for single‑cell RNA sequencing imputation that first detects dropout events with scRecover and then imputes missing values using the non‑parametric missForest algorithm. Benchmarking on public and simulated datasets shows that SCR‑MF delivers robust, interpretable results that match or surpass existing methods while maintaining biological fidelity. Runtime analysis indicates that SCR‑MF balances accuracy with computational efficiency, making it well suited for mid‑scale single‑cell studies.
By Ali Anaissi, Deshao Liu, Yuanzhe Jia, Weidong Huang, Widad Alyassine, Junaid Akram
The paper introduces a pre‑training pipeline that creates transformer‑based imputation specialists for tabular data with specific missingness patterns. By featurizing entries, generating synthetic data with configurable missingness modules, and fitting on millions of synthetic tables, the pipeline produces pattern‑specific models that outperform dedicated methods for each missingness pattern. A default model trained only on MCAR data, TabImpute, remains robust across all tested patterns, and the authors release the pipeline, models, and a new benchmark of 42 datasets and 11 missingness patterns.
By Jacob Feitelberg, Dwaipayan Saha, Kyuseong Choi, Zaid Ahmad, Anish Agarwal, Raaz Dwivedi
arXiv:2609.39613v1 Announce Type: new
Abstract: Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstr...
By Jinwei Li, Michelle Bruch, Daniel Tenbrinck
arXiv:2607. 05613v1 Announce Type: new Abstract: Clinical care often relies on key laboratory indicators, yet real-world patient visits are sparse and tests are ordered irregularly, leading to pervasive missingness.
By Xinrui He, Mengting Ai, Junting Wang, Curtiss B. Cook, Jingrui He
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
By Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya, Benedikt Wiestler, Jana Lipkova
arXiv:2606. 05073v1 Announce Type: new Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values.
By Lixing Zhang, Yidong Ouyang, Weifu Li, Shixiang Zhu, Guang Cheng, Liyan Xie