arXiv:2607. 08803v1 Announce Type: cross Abstract: The push toward large language models for biology (BioLM) has created a need for training corpora that can endow models with a genuine understanding of biology.
arXiv:2605.07938v2 Announce Type: replace Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...
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:2603. 03322v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery.
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
The paper introduces scKITE, a single-cell foundation model that incorporates biological knowledge—cell-level text annotations and gene-level regulatory information—into a shared Transformer encoder via lightweight auxiliary decoders used only during pretraining. This approach provides a new scaling dimension beyond merely increasing data size, enabling the model to achieve superior performance on diverse downstream tasks with only 179,067 pretraining samples, less than 0.5% of the data used by previous strong scFMs. The study demonstrates that knowledge-enhanced pretraining can yield significant gains while reducing computational cost.
arXiv:2609.37675v1 Announce Type: new Abstract: Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and str...
arXiv:2606. 15412v1 Announce Type: cross Abstract: Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge.
arXiv:2609.26347v1 Announce Type: cross Abstract: The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP)...
arXiv:2607. 27258v1 Announce Type: cross Abstract: Plant biosynthetic gene clusters (BGCs) encode specialized-metabolite pathways, yet curated plant BGC labels remain scarce, hindering supervised discovery at genome scale.
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.
arXiv:2605.28868v2 Announce Type: replace-cross Abstract: Metagenomic taxonomic annotation is essential for interpreting complex microbial communities, yet reliable annotation remains challenging und...
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.