Chris Mungall

Phenologs at knowledge-graph scale: a mouse-to-slime-mold catalog of candidate genes for thousands of human diseases

The paper identifies 61,243 phenologs between human diseases and model organism phenotypes, using a hypergeometric method to rank candidate genes and validate their predictive power.

VO: The Vaccine Ontology

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

This study benchmarks large language models against a traditional diagnostic tool for genetic disease diagnosis using 5,213 case reports, finding LLMs lag behind in accuracy.

A custom phenotypic profile for Fanconi anemia: Addressing gaps in existing disease annotations

The paper develops a comprehensive HPO profile for Fanconi anemia by integrating clinical guidelines and LLM-based ontology extraction, identifying significant gaps in existing annotations.

KG-Microbe - Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences

KG-Microbe constructs modular knowledge graphs to integrate microbial data, enabling analysis of complex interactions in health and environment.

GA4GH Phenopacket-Driven Characterization of Genotype-Phenotype Correlations in Mendelian Disorders

The paper introduces GPSEA, a tool using GA4GH Phenopackets to analyze genotype-phenotype correlations in Mendelian disorders, identifying 225 significant associations across 85 cohorts.

Linking international registries to FHIR and Phenopackets with RareLink: a scalable REDCap-based framework for rare disease data interoperability

RareLink enables interoperability between REDCap and global health standards by exporting data to Phenopackets and FHIR, improving rare disease research scalability and data reuse.

Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework

PhEval provides a standardized framework to benchmark phenotype-driven variant and gene prioritisation algorithms using real-world patient data and controlled test corpora.

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

The paper introduces uPheno, a framework for cross-species phenomics integration by harmonizing phenotype data across species and ontologies.

AI-readiness criteria for biomedical data

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery

The paper presents a large-scale corpus of standardized phenopackets for genomic diagnostics and discovery, curated from literature and useful for software testing and genotype-phenotype analysis.

Leveraging Generative AI to Accelerate Biocuration of Medical Actions for Rare Disease

AutoMAxO uses LLMs to semi-automate MAxO curation for rare diseases, identifying 538 confirmed disease-treatment annotations from 4,918 medical abstracts.

FAIR Header Reference genome: A TRUSTworthy standard

The paper introduces FHR, a metadata standard for reference genome data to improve interoperability and data provenance tracking across platforms.

Phenomics Assistant: An Interface for LLM-based Biomedical Knowledge Graph Exploration

Phenomics Assistant is a chat-based interface that uses LLMs to query the Monarch knowledge graph, improving reliability for non-expert users to explore disease-gene-phenotype relationships.

Updates to the Alliance of Genome Resources Central Infrastructure

The paper updates the Alliance of Genome Resources' central infrastructure, adding tools and enhancing data management and curation capabilities.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

This paper introduces OBA, a formalized ontology for biological attributes to facilitate data integration across phenotypic, genetic, and environmental data.

Brain Data Standards - A method for building data-driven cell-type ontologies

The paper introduces a data-driven method to extend cell ontologies for multi-modal single-cell omics data, focusing on the Primary Motor Cortex in humans, mice, and marmosets.

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

The paper introduces a degree-aware edge sampling method to reduce performance inflation in biomedical graph representation learning caused by imbalanced edge degree distributions.

Unifying the Identification of Biomedical Entities with the Bioregistry

The paper introduces the Bioregistry, an open metaregistry that integrates and expands existing biomedical entity registries to improve interoperability and data reuse.

The BRAIN Initiative Cell Census Data Ecosystem: A User's Guide

The paper provides a guide to the BRAIN Initiative Cell Census Network (BICCN) data ecosystem, its tools, and resources for analyzing brain cell types across species.

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

An algorithmic framework for isoform-specific functional analysis

The paper introduces isopret, an algorithm for predicting isoform-specific functions using RNA-seq data, and shows that expression and splicing regulate distinct biological functions.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

The paper uses word embeddings from PubMed to predict which protein kinases are relevant for treating specific cancers using machine learning.

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

The paper describes how the OBO Foundry formalized its principles into operational rules and implemented automated checks to evaluate ontology compliance, improving interoperability and quality.

SvAnna: efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing

SvAnna predicts pathogenicity of structural variants in long-read sequencing data by integrating transcript and regulatory annotations with clinical phenotype data, achieving high accuracy in prioritizing pathogenic variants.

Knowledge Beacons: Web Service Workflow for FAIR Data Harvesting of Distributed Biomedical Knowledge

The paper introduces a REST API called Knowledge Beacon to standardize the discovery and integration of biomedical knowledge from distributed sources.

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

KG-COVID-19 creates customizable knowledge graphs for COVID-19 response by integrating siloed biomedical data into structured formats for various research applications.

Term Matrix: A novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

The paper introduces a Gene Ontology annotation quality control system that identifies and corrects errors in gene product co-annotation by analyzing mutual exclusivity patterns across biological processes.

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.

k-BOOM: A Bayesian approach to ontology structure inference, with applications in disease ontology construction

The paper introduces k-BOOM, a Bayesian method for merging ontologies by estimating logical relationships from mappings, applied to build a unified disease ontology.