Anthony Philippakis

This paper develops a machine learning framework to impute cross-platform proteomic data, improving replication of epidemiological findings and protein quality scoring.

This study investigates genetic factors influencing weight loss from GLP1-RA and bariatric surgery in 10,960 individuals across 9 biobanks, finding no strong genetic associations with GLP1-RA effectiveness but a modest link between BMI polygenic scores and reduced bariatric surgery weight loss.

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

CellBender removes systematic background noise from droplet-based single-cell data using a deep generative model, improving alignment with established gene expression patterns.

This paper introduces MAS-ISO-seq, a method to boost throughput of long-read RNA sequencing for isoform discovery, showing improved detection of differentially spliced genes and cell clustering in T cells.

The paper uses machine learning to analyze myocardial T1 time in UK Biobank data, identifying genetic loci and pathways associated with interstitial fibrosis and related cardiovascular diseases.

This study compares meta-analysis and pooled analysis approaches in cross-cohort GWAS of lipid measures, finding method-specific variants and implications for TRE design and analysis.

FAVOR provides an online resource and tool for functional annotation of variants from whole genome sequencing studies to aid in disease gene prioritization.

The paper performs single-variant and gene-based association analyses of 4,529 phenotypes using exome data from 426,370 UK Biobank individuals, finding associations linked to variant frequency and deleteriousness.

The paper uses deep learning and genome-wide association studies to identify genetic loci associated with right heart structure and function in 40,000 individuals.

This paper develops a cross-modal autoencoder framework to integrate cardiac MRI and ECG data, improving phenotype prediction and enabling MRI imputation from ECGs.

The paper introduces a cloud-based workflow for rare variant analysis in whole genome sequencing studies, enabling scalable and reproducible analysis with functional annotations.

The paper describes AnVIL, a cloud-based platform that enables genomic data analysis without data transfer, improving security, scalability, and collaboration.

The paper uses machine learning to quantify liver fat from MRI data and identifies genetic variants associated with liver fat accumulation and related diseases.

This study uses deep learning to analyze aortic dimensions from MRI and identifies genetic loci associated with aortic diameter, highlighting SVIL as a key gene and developing a polygenic score for aneurysm risk.

The paper examines how polygenic risk modifies the penetrance of monogenic variants in coronary artery disease, breast cancer, and colorectal cancer across multiple cohorts.

Four levers for cheaper cloud genomics on FireCloud: size virtual disks dynamically, tune VM provisioning from monitoring data, buy preemptible machines, and parallelise tasks at the right width.