All Times EDT
Keywords: Statistical Learning, High-dimensional data, knowledge-guided, precision medicine
High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer and Alzheimer’s disease at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with methods that do not use biological knowledge. In this talk, I will review current knowledge-guided statistical learning methods and their applications to precision medicine and discuss future research directions.