Manyan Huang

Topic: Transcriptional-Perturbation Integration for Therapeutic Response Prediction

Project summary: Accurate prediction of therapeutic response remains a critical challenge in precision oncology. Existing gene expression foundation models, while powerful, lack cancer-specific fine-tuning, limiting their ability to capture tumor-relevant regulatory patterns essential for therapeutic response prediction. Additionally, the translational gap between preclinical models and clinical patient outcomes continues to limit their application in therapeutic decision-making. We propose developing a computational framework that integrates cancer-adapted transcriptional modeling with perturbationderived drug signatures to predict patient-level therapeutic responses. The approach combines foundation model adaptation, multi-modal embedding integration, and transfer learning strategies to capture drug sensitivity patterns across cancer contexts. This framework aims to bridge preclinical drug characterization with clinical decision-making, offering potential for therapeutic prioritization, biomarker identification, and discovery of novel treatment opportunities for under-targeted patient populations.

Biography: Manyan Huang, Ph.D., is a Postdoctoral Associate in the Gerstein Lab at Yale University. Her research focuses on integrative computational analysis of gene expression regulation in brain cancer and neuropsychiatric disorders. She leverages multi-omic datasets and AI-driven foundation models to uncover disease mechanisms and predict therapeutic responses. Dr. Huang earned her Ph.D. from Indiana University under the supervision of Dr. Ming Li, specializing in statistical genetics and genetic epidemiology. Her doctoral research focused on genome-wide association studies and gene-based methods for detecting rare variant effects in complex diseases.

Yale-Mentor Professor Mark Gerstein, BI-Mentor Dr. Youli Xia