arXiv:2606. 00483v1 Announce Type: cross Abstract: Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 13556v1 Announce Type: new Abstract: Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of individual behavioral data before they can distinguish constitutional variation from environmentally driven deviation.
The study evaluated nine transcriptomic models—five bulk RNA‑seq and four single‑cell RNA‑seq—designed to predict response to immune checkpoint inhibitors. Across independent datasets, bulk models performed near chance while single‑cell models offered only modest gains, and pathway analyses revealed inconsistent biomarker signals. The results highlight the limited cross‑cohort robustness and biological consistency of current transcriptomic ICI predictors.
arXiv:2607. 16053v1 Announce Type: cross Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains challenging under practical and methodological constraints.
arXiv:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.
arXiv:2606. 17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors.
PopPert is a framework that models population-level joint gene expression distributions to predict transcriptional responses to perturbations in single-cell RNA sequencing data. By using a low‑rank Gaussian Copula, it captures gene co‑expression patterns and eliminates the need for cell‑to‑cell correspondence, thereby reducing sensitivity to single‑cell noise. Across multiple benchmarks, PopPert outperforms existing methods in differential expression recovery, perturbation effect estimation, and distribution matching, demonstrating the effectiveness of population‑level joint distribution learning for unpaired single‑cell data.