Uploaded July 2022 | Updated September 2026, 3 days ago
Abstract
COVID-19 is the tip of the iceberg with many other unsolved biomedical problems, such as cancer early identification and finding side effects of new drugs. These problems seem to be independent of each other and have so far been tackled by different biologists. In this talk, I will argue that behind these different biomedical problems is the same computational challenge, that is, how to understand and predict in never-before-seen situations. In addition to powerful predictive models, what is really needed are tools that generalize well to new drugs, new diseases, and new cohorts. My talk will discuss novel machine learning methods developed to tackle two kinds of never-before-seen situations: never-before-seen class and never-before-seen cohort. I will first introduce how we classify samples into never-before-seen classes by embedding noisy and large-scale biomedical ontologies, resulting in new discoveries in protein functions, cell types, and rare diseases. Next, I will introduce our solution to understand and characterize a never-before-seen cohort. Instead of finding which features are important, we answer the question of why these features are important using a novel multiscale biomedical knowledge graph. This multiscale knowledge graph is constructed using millions of scientific papers and millions of experimental associations, providing up-to-date and scalable evidence for observations in our multiscale biomedical world.
Bio
Sheng Wang is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at University of Washington. He was a postdoc researcher at Stanford University School of Medicine and received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He is interested in using machine learning and natural language processing to advance biomedical sciences. The current focus of his research is new methods for predicting and understanding never-before-seen situations in biomedicine. His research has resulted in real-world impact in biology, medicine, and healthcare, and is used by major biomedical institutions, including Chan Zuckerberg Biohub, NIH National Center for Advancing Translational Sciences, UCSD School of Medicine, China Academy of Medical Sciences, and Mayo Clinic. His research is also recognized through several paper awards and fellowships, including AMIA 2020 Year-In-Review, ISMB best student paper candidate, C. L. and Jane W.-S. Liu Award, and 3M Foundation Fellowship.
Abstract
COVID-19 is the tip of the iceberg with many other unsolved biomedical problems, such as cancer early identification and finding side effects of new drugs. These problems seem to be independent of each other and have so far been tackled by different biologists. In this talk, I will argue that behind these different biomedical problems is the same computational challenge, that is, how to understand and predict in never-before-seen situations. In addition to powerful predictive models, what is really needed are tools that generalize well to new drugs, new diseases, and new cohorts. My talk will discuss novel machine learning methods developed to tackle two kinds of never-before-seen situations: never-before-seen class and never-before-seen cohort. I will first introduce how we classify samples into never-before-seen classes by embedding noisy and large-scale biomedical ontologies, resulting in new discoveries in protein functions, cell types, and rare diseases. Next, I will introduce our solution to understand and characterize a never-before-seen cohort. Instead of finding which features are important, we answer the question of why these features are important using a novel multiscale biomedical knowledge graph. This multiscale knowledge graph is constructed using millions of scientific papers and millions of experimental associations, providing up-to-date and scalable evidence for observations in our multiscale biomedical world.
Bio
Sheng Wang is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at University of Washington. He was a postdoc researcher at Stanford University School of Medicine and received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He is interested in using machine learning and natural language processing to advance biomedical sciences. The current focus of his research is new methods for predicting and understanding never-before-seen situations in biomedicine. His research has resulted in real-world impact in biology, medicine, and healthcare, and is used by major biomedical institutions, including Chan Zuckerberg Biohub, NIH National Center for Advancing Translational Sciences, UCSD School of Medicine, China Academy of Medical Sciences, and Mayo Clinic. His research is also recognized through several paper awards and fellowships, including AMIA 2020 Year-In-Review, ISMB best student paper candidate, C. L. and Jane W.-S. Liu Award, and 3M Foundation Fellowship.
