Current Bio3 Seminar Series

Abstract: Are you interested in how biostatistics connects with the pharmaceutical industry, regulatory science, and health innovation? The ASA Biopharmaceutical Section (BIOP) offers a wide range of opportunities for students and faculty to grow professionally, connect with leaders in the field, and access resources to support their research. In this session, BIOP representatives will give an overview of awards, conferences, and our external funding program. We will then open the floor for an interactive Q&A to discuss career paths, networking, and ways to get involved with BIOP.

Location: Building D, Warwick Evans Conference Room

Abstract: Biomedical studies now generate vast amounts of molecular data, ranging from thousands of biomarkers to millions of genetic variants, creating major challenges in distinguishing meaningful signals from noise. Patient outcomes vary widely, and methods that focus only on averages can overlook biomarkers linked specifically to poor or favorable outcomes. One direction of this work applies quantile regression to study different parts of the outcome distribution, allowing the identification of markers relevant at the extremes. Another challenge arises in the high-dimensional setting, where some biomarkers may not appear important individually but become informative when considered jointly. To address this, I will introduce a conditional screening approach developed to uncover these hidden signals.

The presentation will also include an overview of the NIH Intramural Research Program, which supports postbaccalaureate, predoctoral, and postdoctoral fellows in biostatistics and related fields, working closely with investigators across diverse areas of biomedical research.

Location: Building D, Warwick Evans Conference Room

Abstract: Transformative AI models are powerful tools for integrating and mining large-scale biomedical and omics data, and have been revolutionizing research in precision medicine.  In this talk, I will present novel transformative AI models that we have developed to combine large language models (LLMs) with graph-based AI to integrate and analyze vast omics datasets for identifying disease targets, inferring signaling pathways, and predicting effective drugs and drug combinations. A key component of the novel AI models is the novel text-numeric graph (TNG) or text-omic signaling graphs (TOSGs), a novel structure in which graph entities and associations carry both textual and numeric attributes. I will also introduce an AI multi-agent system that we have developed to accelerate biomedical discovery by unifying omics data analysis, literature-based deep search, and reasoning to generate novel scientific hypotheses. I will then showcase the applications of these novel AI models with analysis of heterogeneous pharmacogenomics data for precision medicine.

Location: Building D, Warwick Evans Conference Room