arXiv:2510. 17064v4 Announce Type: replace Abstract: Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures.
arXiv:2601. 12805v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks.
arXiv:2603. 11872v3 Announce Type: replace-cross Abstract: Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language.
NS-Copilot is a large‑language‑model driven multi‑agent system designed to automate neuroscience data analysis. It integrates domain‑specific pre‑trained models for modalities such as EEG and extracellular spike data, and uses a natural‑language interface to orchestrate agents that plan, generate code, and synthesize results. In benchmarks on Alzheimer’s, Parkinson’s, and working‑memory spike decoding, the system consistently outperformed strong baselines across multiple trials.
FlyAOC is a benchmark that tests AI agents on end‑to‑end ontology curation of Drosophila scientific literature. Given a gene symbol, a brief description, a large paper corpus, and ontology resources, agents must search for evidence and produce structured annotations such as function terms, expression patterns, and historical synonyms. The benchmark contains 7,397 expert‑curated annotations across 100 genes and evaluates different agent harnesses, revealing system‑level failure modes that single‑task evaluations miss.
arXiv:2605. 07022v3 Announce Type: replace Abstract: Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage.
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:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.
OmniVCBench is a figure‑centric, source‑traceable benchmark designed to evaluate the interpretation component of Artificial Intelligence Virtual Cells (AIVCs). It comprises 6,077 curated question–answer pairs drawn from scientific figures and experimental contexts, organized into three scientific reasoning tasks that mirror the AIVC Predict–Explain–Discover agenda. The benchmark also introduces AIVC‑Judge, a task‑conditioned MLLM‑as‑a‑judge framework with reference‑aware rubrics, and a Model‑Derived Hard‑Negative Mining strategy to generate multiple‑choice distractors for efficient evaluation.
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...
OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.
arXiv:2610.01393v1 Announce Type: cross Abstract: Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary...
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.