After joining Brown and CCMB in 2023, Ying Ma has been working hard to address genomic challenges in spatial transcriptomics with integrative deep learning and statistical methods. She has recently received three grants over $1M each from the NSF and NIH as the lead or sole PI to continue her innovative work.
Integrative Computational Models for Decoding Disease Mechanisms and Predicting Drug Synergies in Spatial Transcriptomics
MIRA ESI R35 Outstanding Investigator Award
This project will develop novel statistical and computational methods to integrate Spatially multi-omics data with clinical, pharmacogenomic, and GWAS data to uncover how cellular spatial organization influences critical clinical phenotypes in complex tissues. By jointly modeling pharmacogenomic data in the context of cellular spatial organizations and pinpointing disease-associated spatial patterns, the proposed tools will overcome current limitations in leveraging heterogeneous data sources. Ultimately, this work will advance our understanding of how spatial heterogeneity drives disease progression and shapes therapeutic responses.
NSF/BIO-UKRI/BBSRC: Integrative Deep Learning and Statistical Models for 3D Multimodal Analysis of Brain Structure
Collaborative Research: SCH: Protecting Privacy and Promoting Fairness in Advanced Genomic Research using Federated Learning
This project represents a significant step forward in advancing federated learning for large-scale, privacy-preserving analysis of biological data across multiple institutions – enabling collaboration without the need for data sharing. This project will develop a series of statistical and computational methods that enables secure, collaborative analysis of genomic and genetic data without requiring data to be shared.
