arXiv AI
SeqMaestro: From nucleotide sequences to biological hypotheses through interpretable machine learning
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Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
arXiv:2509.12266v3 Announce Type: replace-cross Abstract: We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core...
arXiv AI
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Fusing Sequence Motifs and Pan-Genomic Features: Antimicrobial Resistance Prediction using an Explainable Lightweight 1D CNN-XGBoost Ensemble
arXiv:2509.23552v2 Announce Type: replace-cross Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
arXiv AI
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You Only Train Once: Differentiable Subset Selection for Omics Data
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Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks
The paper introduces OmicsBench, a new reasoning benchmark for multi‑omics sequences that includes 1,160 expert‑validated questions across DNA regulation, RNA processing, and protein function tasks, requiring traceable evidence chains. Evaluation of 17 large language models shows that scientific LLMs, while more accurate in classification, often lack valid evidence, suggesting shortcut learning. To address this, the authors propose tool‑augmented on‑policy distillation (TA‑OPD), a post‑training method that improves both evidence grounding and predictive performance across five Qwen3.5 models of varying sizes.
arXiv Machine Learning
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Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions
arXiv:2607. 00931v1 Announce Type: new Abstract: Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predictive accuracy, but also on their capacity to generate reliable biological insights.
arXiv Machine Learning
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Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
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