Leigh Carmody

The paper identifies pathogenic variants in 15.8% of CP patients and finds only 16 of 60 genes have significant CP association.

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.

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

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.

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

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

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