arXiv:2606. 01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.
By Xinyu Yuan, Xixian Liu, Jianan Zhao, Yashi Zhang, Hongyu Guo, Jian Tang
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.
By Jie Ying, Zhefan Wang, Zihong Chen, Zhengqing Li, Jinzhe Li, Gang Li, Jian Liu, Fang Hu, Tao Luo, Zhonghang Yuan, Wanli Ouyang, Stan Z. Li, Fan Yang, Nanqing Dong
arXiv:2603. 02274v3 Announce Type: replace-cross Abstract: Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant but pharmacological response samples are sparse.
By Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang
The paper introduces HypoKG, a unified biochemical knowledge graph built from KEGG, Rhea, and UniProt, and uses it to benchmark 13,200 biomedical hypotheses generated by six large language models (LLMs). By varying the biological information provided—source enzyme only, full biological path, or source and disease endpoint—the study finds that LLMs produce higher-scoring hypotheses when given minimal information, but these are less evidence‑grounded. When supplied with the full biological path, the models generate hypotheses that align more closely with known mechanistic relationships, a phenomenon the authors term evidence‑disciplined reasoning, which is confirmed by shuffling intermediate path steps.
"whyItMatters":"The study demonstrates that knowledge graphs can both uncover novel disease–enzyme pairs and guide LLMs to reason more accurately from evidence, improving the reliability of AI‑generated biomedical hypotheses."
By Dominic Okonkwo, Adetayo Okunoye, Ismailcem Budak Arpinar
arXiv:2609.06779v1 Announce Type: cross
Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...
By Zijie Liu, Hongxuan Li, Zhen Tan, Jinhao Duan, Baixiang Huang, Zunpeng Liu, Kai Shu, Tianlong Chen
arXiv:2607. 18777v1 Announce Type: new Abstract: Evaluating machine learning in scientific domains requires separating correct predictions from correct reasons under realistic distribution shifts.
By Dongkwan Kim, Yiming Gao, Yining Yang, Yang Shen