Akansha Srivastava

Deciphering Spatially Resolved Pathway Heterogeneity in Ovarian Cancer Post-Neoadjuvant Chemotherapy

This study analyzes spatial transcriptomics data from ovarian cancer patients post-chemotherapy to identify spatially distinct pathway activities and their association with treatment response.

DeepGraphMut: A graph-based deep learning methodfor cancer prognosis using somatic mutation profile

DeepGraphMut uses graph-based deep learning to predict cancer prognosis from somatic mutation data, identifying subtypes and achieving high survival prediction accuracy across 16 cancer types.

A Single-Cell Network Approach to Decode Metabolic Regulation in Gynecologic and Breast Cancers

This study uses a single-cell network approach to analyze metabolic regulation in gynecologic and breast cancers, identifying key transcriptional regulators and metabolic pathways involved in tumor heterogeneity.

Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

This study identifies two metabolic subtypes of endometrial cancer using transcriptomics and metabolic modeling, linking them to clinical outcomes and genomic alterations.