Melissa Anne Haendel

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.

The paper introduces EC-KG, a unified biomedical knowledge graph for drug repurposing that integrates multiple existing knowledge graphs to enhance coverage and uncover novel mechanistic pathways.

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.

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.

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

The paper evaluates two self-phenotyping tools, showing that GenomeConnect provides more accurate phenotype profiles while Phenotypr offers greater precision and user preference for its autocomplete format.

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

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

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

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.

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

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.

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

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

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

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.

SQUIRLS prioritizes splice variants by calculating interpretable features and achieving higher accuracy and speed than existing methods in simulated exomes.

The paper identifies sex-biased differential alternative splicing across multiple human tissues and links it to gene function and estrogen response.

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

The paper introduces LIRICAL, a likelihood ratio-based framework for genomic diagnostics that improves accuracy and interpretability in phenotype-driven rare disease diagnosis.

The paper harmonizes clinical interpretations of cancer genomic variants across six knowledgebases, improving overlap and matching with patient cohorts.

FAIRshake evaluates the FAIRness of research digital resources through community-driven metrics, automated assessments, and visualization tools, revealing opportunities for improvement.

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

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

The paper creates synthetic clinical cardiovascular data and a curriculum to teach data science fundamentals, finding mixed student performance in applying machine learning techniques.

The paper outlines ten lessons for designing, provisioning, and reusing persistent identifiers to improve data integration in life sciences.

The paper describes the Monarch Initiative, a platform that integrates multimodal data across species to enhance genotype-phenotype analysis and precision medicine.