Vida Ravanmehr

Diverse ancestral representation improves genetic intolerance metrics

This study shows that genetic diversity improves the performance of intolerance metrics in identifying disease-related genes, with multi-ancestry data outperforming European-only datasets.

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.

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.

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.

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

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

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.

ChIPWig: A Random Access-Enabling Lossless And Lossy Compression Method For ChIP-Seq Data

ChIPWig compresses ChIP-seq Wig data with lossless and lossy modes, achieving high compression rates while preserving peak calling and motif discovery accuracy.