arXiv Machine Learning
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
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arXiv AI
Jun 11
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
arXiv AI
3d ago
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
arXiv:2610.03693v1 Announce Type: new Abstract: Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete r...
arXiv Machine Learning
Jul 21
Tabular Foundation Models Can Do Survival Analysis
arXiv:2601. 22259v2 Announce Type: replace Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event of interest occurs.
arXiv AI
Jun 4
You Only Train Once: Differentiable Subset Selection for Omics Data
arXiv:2512. 17678v2 Announce Type: replace-cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.
arXiv Machine Learning
Jul 1
Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
arXiv:2606. 31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design.
arXiv AI
Oct 1
CellMSA: Context Modeling for Single-Cell Representation Learning
CellMSA introduces a novel single‑cell representation learning framework that leverages a multiple‑sequence‑alignment‑inspired context model. For each target cell, it retrieves relevant cells across batches and related cell types, summarizing cross‑cell patterns into a context‑dependent gene‑pair representation that is fed into a pair‑aware encoder. Pretraining on a massive human single‑cell corpus (≈109 million cells) and subsequent benchmarks demonstrate consistent performance gains over existing methods.