Gennady Gorin

The paper develops a model to distinguish biological from technical spatial variability in spatial transcriptomics data and introduces the stipple software for analysis.

The paper analyzes empty drops in scRNA-seq to identify sequencing artifacts and reveals the prevalence of neuropeptide mRNA, suggesting potential physiological relevance.

DEPower provides a web tool for power analysis in RNA-seq experiments based on DESeq2 assumptions, enabling researchers to determine sample size requirements for detecting differential expression.

The paper reports endogenous XIST expression in male heart samples across multiple single-cell RNA sequencing studies and links it to heart disease and myocardial infarction.

This paper introduces Chronocell, a method for trajectory inference using process time modeling, showing it can interpolate between trajectory inference and clustering and assess dataset suitability.

This paper proposes a biophysical model for analyzing ATAC-seq data and evaluates its benefits for integrating multiome data with single-cell RNA-seq.

The paper introduces meK-Means, a method for clustering cells from multimodal sequencing data by integrating biophysical models of transcription to identify shared cellular states.

This paper uses stochastic biophysical modeling to analyze perturbation data and uncover transcriptional dynamics and kinetic responses in cells.

This paper introduces neural network approximations for solving steady-state distributions in multi-species transcriptional models, enabling efficient inference of biophysical parameters from transcriptomic data.

The paper introduces Monod, a Python package that integrates single-cell RNA sequencing data using biophysical models of transcription to distinguish biological and technical variation and reveal regulatory processes.

The paper extends RNA velocity to incorporate protein data, enabling temporal cell state inference from single-cell multiomics experiments.