Russell S. Schwartz

This study uses discrete diffusion models to generate tumor phylogenies, finding that mid-scale models outperform deeper ones and that diverse training improves cross-regime generalization.

This study shows that elevated cfDNA and fragment shortening in cancer are due to saturated hepatic clearance, not tumor burden alone.

The paper estimates the number of mutations needed to reconstruct clonal lineage trees from large datasets, showing that modest mutation counts suffice for accurate reconstruction.

This study develops a computational method to infer cancer progression risk by analyzing tumor phylogenetics and mutation data from TCGA cohorts, revealing general mechanisms and cohort-specific variability.

The paper develops methods to improve marker selection for ctDNA analysis by refining phylogenetic models and quantifying clonal frequencies through optimization.

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.

This paper introduces TUSV-int, a method that integrates bulk DNA-seq and scRNA-seq to improve deconvolution and phylogenetic inference for cancer clonal structure.

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.

This paper develops a simulation-based optimization framework for designing genomic studies focused on clonal evolution, particularly in cancer, to optimize resource allocation and study design.

The paper introduces CITRUS, a deep learning model that infers transcriptional programs influenced by somatic alterations across 17 cancer types using genomic, transcriptomic, and epigenomic data from The Cancer Genome Atlas.

The paper introduces a simulator for generating synthetic data to optimize study designs for analyzing somatic evolution in cancer and other diseases.

ConTreeDP computes clonal consensus trees from cancer genomic data by solving the Maximum Directed Partition Support Consensus Tree problem, showing efficiency and accuracy.

This paper introduces DeepGRMF, a deep learning model that predicts cell-drug sensitivities by integrating drug structures, signaling impacts, and cell states, outperforming existing methods on cancer datasets.

The paper develops computational methods combining sequencing and miFISH data to improve accurate inference of clonal copy number evolution in tumors.

The paper develops methods to deconvolve tumor copy number data by integrating bulk and single-cell sequencing data, improving accuracy over bulk data alone.