R. Schwartz

Predicting locus phylogenetic utility using machine learning

This study uses machine learning to predict locus phylogenetic utility, improving topological accuracy and statistical support by filtering out noisy loci from phylogenomic datasets.

Determining Optimal Placement of Copy Number Aberration Impacted Single Nucleotide Variants in a Tumor Progression History

This paper introduces DETOPT, a method for accurately placing SNVs affected by CNAs in tumor progression trees, showing improved accuracy on simulated data and biologically plausible results on breast cancer metastases.

Structure-enabled enzyme function prediction unveils elusive terpenoid biosynthesis in archaea

The paper introduces EnzymeExplorer, a machine-learning pipeline that predicts enzyme function, discovering new terpenoid synthases in archaea and expanding their known taxonomic distribution.

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

Sc-TUSV-ext improves clonal lineage reconstruction by integrating SNV, CNA, and SV from single-cell DNA sequencing data, showing better accuracy on synthetic and real data.

Modeling the effect of spatial structure on solid tumor evolution and ctDNA composition

This study models how spatially variable cell death in solid tumors affects ctDNA composition and clonal representation, showing potential biases in liquid biopsy profiles.

SARS-CoV-2 infection results in lasting and systemic perturbations post recovery

This study compares short- and long-term systemic responses to SARS-CoV-2 and IAV in golden hamsters, finding persistent inflammation in olfactory tissues post-recovery.

Improving personalized prediction of cancer prognoses with clonal evolution models

This study develops a computational framework to predict cancer progression using mutator phenotypes derived from tumor phylogenies, showing improved predictive power over traditional clinical and driver-centric features.

Deconvolution and phylogeny inference of structural variations in tumor genomic samples

This paper introduces a method to infer tumor phylogeny and deconvolute structural variations from bulk genomic data, showing it works well on simulated and breast cancer data.