arXiv:2606. 24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The article presents a new semantic model for representing scientific evidence, specifically tailored to genetics, that extends existing standards by adding fine‑grained, domain‑specific structure. It aligns with FHIR Evidence and SEPIO, incorporates a compact vocabulary validated by SHACL, and was tested in a human‑AI annotation pilot on six genetics papers, producing 28 evidence items and 95 source‑anchored assertions. The authors argue that this model advances trustworthy, AI‑ready infrastructure for variant interpretation by providing a reference data model and validation schema for genetic evidence.
arXiv:2511. 09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood.
The paper presents an autonomous AI agent that combines ReAct and Retrieval-Augmented Generation to classify the severity of genetic diseases using 10,211 Human Phenotype Ontology terms. It applies ACMG severity guidelines and ACOG quality-of-life criteria, retrieving PubMed literature to produce interpretable reasoning chains and verify claims. The agent achieved 93.55% accuracy on phenotype-level classification and identified 3,283 autosomal recessive gene pairs with severe presentations, with 95.2% concordance in external validation.
BaseCamp is an agentic AI framework that automates the decision layer of DNA sequencing pipelines by deploying six specialized AI agents for tasks such as sample intake, quality control, alignment, variant calling, annotation, cross‑stage monitoring, and reporting. The agents rely on established bioinformatics tools for actual sequence analysis, while using fine‑tuned, domain‑specialized large language models to select, configure, and interpret these tools’ outputs, ensuring reproducibility and local data privacy. Evaluation demonstrates that the agents’ configurations align with expert practice, provide an explicit filtering ledger for traceability, and detect anomalies that traditional monitoring may miss.
arXiv:2601. 21800v4 Announce Type: replace Abstract: We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks.
arXiv:2606. 16149v1 Announce Type: new Abstract: Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases.