2 citations · 3 across the 4 of their papers we have counts for
5 papers
Learning biophysical models of gene regulation with probability flow matching
Suryanarayana Maddu, Victor Chardès, Michael J. Shelley
Cellular differentiation is governed by gene regulatory networks, the high-dimensional stochastic biochemical systems that determine the transcriptional landscape and mediate cellu…
Random Matrix Theory-guided sparse PCA for single-cell RNA-seq data
Victor Chardès
Single-cell RNA-seq provides detailed molecular snapshots of individual cells but is notoriously noisy. Variability stems from biological differences and technical factors, such as…
Inferring stochastic dynamics with growth from cross-sectional data
Stephen Zhang, Suryanarayana Maddu, Xiaojie Qiu +1
Time-resolved single-cell omics data offers high-throughput, genome-wide measurements of cellular states, which are instrumental to reverse-engineer the processes underpinning cell…
Inferring biological processes with intrinsic noise from cross-sectional data
Suryanarayana Maddu, Victor Chardès, Michael. J. Shelley
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explo…
Stochastic force inference via density estimation
Victor Chardès, Suryanarayana Maddu, Michael J. Shelley
Inferring dynamical models from low-resolution temporal data continues to be a significant challenge in biophysics, especially within transcriptomics, where separating molecular pr…