Sachit D. Saksena

Temporal Biodynamics identifies stage-relevant targets and biomarkers by modeling disease progression using AI, improving detection of disease-associated genes and proteins over traditional methods.

This study identifies mutation and transcriptional state compatibility in Ph+ ALL MRD, showing that targeting these interactions can overcome BCR::ABL1 independence and improve MRD monitoring.

This paper introduces PRESCIENT, a generative model for inferring cell differentiation landscapes from single-cell time-series data, showing improved prediction of cell fate and perturbation responses.

MaGIC identifies monoallelic expressed genes using chromatin signatures from ChIP-seq data, providing a tool for analyzing MAE in human and mouse cells.