Assessing the Reliability of LLM-Generated Phenotype-Genotype Associations Through External Validation

From Data Curation to Risk Reporting: A Pipeline for Polygenic Risk Scores

PregBase: A comprehensive knowledge base for clinically relevant knowledge representation and biomarker prediction in pregnancy

Pregnancy complications are a leading cause of maternal and neonatal mortality worldwide. Understanding their underlying mechanisms is hindered by dispersed evidence across thousands of studies and complex biological interactions. We present PregBase, a comprehensive knowledge base for pregnancy research comprising automated extraction, validation, and exploration. PregBase was constructed by utilising large language models (LLMs) to extract relationships from literature; 13 LLMs were benchmarked across seven prompting strategies, with ensemble shuffle prompting outperforming single-strategy alternatives. A three-tier validation pipeline combining ontology mapping, graph neural networks, and statistical analysis produced PregKG, a knowledge graph containing 64,087 associations between 13,303 biomedical entities spanning 155 semantic types and 50 vocabularies across 8 relationship types from 8420 articles. Link prediction validated PregBase's inference capability beyond extracted knowledge, recovering established clinical interventions, reconstructing canonical hormonal pathways across maternal-placental-fetal compartments, and identifying novel biomarker candidates for preterm birth, gestational diabetes, and preeclampsia, supported by genetic and expression databases. An interactive web interface (https://pregknowledgebase.com) has been created to enable further exploration via conversational queries. This work provides a scalable foundation for systematic discovery in maternal health research.

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine

A clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine, focusing on factors that determine model robustness and clinical utility, and common sources of failure in real-world genomic AI systems.

aiDIVA – hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models

aiDIVA is presented, an ensemble-AI combining statistical and machine learning models trained on genomic and phenotypic data to identify causal variants among tens of thousands per patient, and applies a random forest model to classify pathogenicity and generates evidence-based scores for dominant and recessive diseases.

TRI-IVIS for Genome Knowledge Related to SNP Analyses