Teresa M. Przytycka

Fitting a linear ODE with an equilibrium term via its matrix-exponential solution, not finite differences, raises TF-network AUROC over competing methods on simulated and scRNA-seq data.

The paper identifies sex-specific allometric strategies in Drosophila organs, showing how cell size and number differ between sexes to drive body size differences.

ReDeNovo infers both known and novel mutational signatures from cancer genomes, outperforming existing methods and identifying new signatures in real data.

The paper introduces RePrint, a method to infer shared DNA repair mechanisms between mutational signatures, outperforming existing methods and enabling guilt-by-association predictions.

SpacePhenotyper transfers predictive gene expression-phenotype relationships from bulk data to spatial transcriptomics data to analyze clinical phenotypes in spatial regions of tumors.

The paper models gene expression adaptation in tumor subclones using stochastic processes and finds non-genetic mechanisms drive subclonal evolution and treatment response.

The paper investigates how somatic Transformer and sex chromosome karyotype pathways regulate gene expression during Drosophila gonad development, showing their combined and exclusive effects on germ cell development.

The paper identifies two lung cancer subtypes defined by APOBEC mutagenesis levels and shows their association with different genetic mutations, aging, and immune responses.

TranNet identifies predictors of tumor progression by analyzing gene expression differences between tumor and adjacent normal tissues using TCGA data.

The paper introduces a network-based method to uncover relationships between mutational signatures and biological processes in cancer data.

The paper provides single nucleus and single cell RNA-seq data from Drosophila testes to study spermatogenesis, identifying rare cell types and developmental transitions.

The paper creates a single-cell transcriptomic atlas of the adult fruit fly, identifying over 250 cell types and analyzing gene signatures and sexual dimorphism across tissues.

This study uses mutational signatures to assess the impact of smoking on biological processes related to SARS-CoV-2 infection and disease progression in COVID-19.

RepairSig identifies DNA repair deficiency mutational signatures in cancer by modeling interactions between mutagenic processes, replacing multiple signatures with a single representative one in breast cancer.

The paper identifies functional pathways associated with mutational signatures in breast cancer, revealing differences in the etiology of APOBEC and clock-like signatures.

The paper introduces NetREX-CF, a method combining collaborative filtering and sparse network component analysis to reconstruct gene regulatory networks, outperforming existing methods.

The paper introduces JUDI, a bioinformatics pipeline framework that reduces scripting effort for parameterized workflows by leveraging DBMS principles, demonstrating a fivefold reduction in scripting for HT-SELEX data analysis.

This paper introduces sequence-dependent models for mutation signatures, improving the resolution of mutation signature activity in cancer by accounting for dependencies between mutations.

Co-SELECT identifies DNA shape's non-specific contribution to TF binding in vitro by analyzing HT-SELEX experiments, showing shape features can drive weak binding even without sequence motif similarity.

PopCorn identifies and maps sub-populations across single-cell experiments with high precision.

AptaBlocks designs RNA sticky bridges to connect aptamers and therapeutic cargoes, enabling efficient RNA-based drug delivery systems.

The paper assesses confidence and stability in decomposing mutational signatures in breast cancer genomes and identifies stable, unstable, and previously missed signatures.

NetREX constructs context-specific regulatory networks by integrating expression data and context-agnostic networks, showing improved biological consistency in Drosophila data.