arXiv Machine Learning
Agentic Literature Extraction Augments Targeted Protein Degradation Databases
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arXiv AI
Oct 2
Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
The study evaluates how well large language models (LLMs) can annotate missing metadata in PubChem’s ~2 million bioassays, focusing on BioAssay Ontology (BAO) assay format and detection method fields. It finds that 36 % of assays lack an assay format, 89 % lack a BioAssay type, and over 99.9 % lack any BAO‑mapped format or detection technology term, highlighting a critical sparsity in metadata. Seven open‑source and proprietary LLMs achieve recall ≥0.96 for biochemical and cell‑based assay formats, with similar performance for detection technology, though disagreements rise for under‑represented classes and often stem from inconsistencies in silver labels rather than LLM errors. A qualitative test with an industrial curator shows LLM‑generated evidence can prompt revisions of existing labels, indicating that LLMs can flag potentially mislabeled assays.
arXiv Machine Learning
Oct 27
A curated multi-endpoint drug-protein bioactivity dataset with construct-aware protein targets
arXiv:2610.32490v1 Announce Type: new Abstract: Public drug-protein bioactivity resources commonly identify targets by UniProt accession, although one accession may represent distinct experimental se...
arXiv AI
Jul 7
DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment
arXiv:2408. 13378v5 Announce Type: replace Abstract: Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature.
arXiv AI
Sep 1
From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction
arXiv:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
arXiv Computation and Language
Oct 15
ANCHOR-RE: An Agentic Biomedical Relation Extraction Framework with Retrieval Augmentation and Error-Driven Verification
arXiv:2608.03154v2 Announce Type: replace Abstract: Biomedical relation extraction (BioRE) supports knowledge graph construction and hypothesis generation. In real-world settings, most candidate enti...
arXiv AI
Aug 18
Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs
arXiv:2608. 14765v1 Announce Type: new Abstract: Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations.