Clarifying the Mysteries of Choosing Statistical and Machine-Learning Methods in Genomics Research

Lecture by Jingyi Jessica Li, professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at UCLA. Li leads a research group called the Junction of Statistics and Biology, where she and her students focus on developing statistical and computational methods to answer important questions in biological and biomedical sciences and to extract key information from genomics and health-related data.

In this lecture, Li will clarify common confusions in genomics data analysis by connecting cutting-edge genomics research questions with fundamental statistical and machine-learning methods. In particular, she will focus on the distinctions and choices among the methods that are apparently similar but fundamentally different, so that quantitative genomics researchers will have clear guidelines to follow in their development of bioinformatics tools.

Read on hms.harvard.edu

Email Share