Development of Novel Multi-Task Prediction Methods for Large Scale Genomic Data

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My research focuses on developing novel statistical and computational methodologies and software for multi-task prediction problems and their application in the analysis of genomic data. Background: Tremendous genomic data are being generated by various high-throughput experimental technologies, such as Next Generation Sequencing. Common features of most genomic data are high dimensional (up to tens of thousands for gene expression data or up to millions for Genome-Wide Association Studies) and high correlation. Since those data usually have small and moderate sample sizes, ranging from hundreds to thousands, signals are sparse, creating challenges for finding important disease-associated genomic predictors. Traditional statistical methods are mainly designed for low dimensional data. And the popular deep learning requires a large sample to train the network to have better prediction performance. Therefore, for such data, novel methods based on statistical learning are in high demand. Particularly, there are gaps in developing multi-task learning methods, which simultaneously predict multiple research outcomes and select important genomic predictors from a large pool. Objectives: I recently developed a novel algorithm based on revising the well-known stacking algorithm and established that it outperforms the neural network and other popular statistical learning methods in genomic data for moderate sample sizes. I propose extending this work to handle other common types of outcomes and plan to incorporate the proposed methods into my previously developed publicly available R package, called MTPS. The software will become a unified and comprehensive software platform dealing with multi-task problems with high-dimensional correlated data. I will conduct the proposal through training highly qualified personnel (HQP) under the guidelines of equity, diversity, and inclusion. Impact: My software, MTPS, has been downloaded 4000+ times since released in February 2020. By incorporating the proposed methods, we will add new features to it to solve a broader range of problems. My novel methods and software will become one of the most popular tools for multi-task prediction in the future. I will make significant contributions to various subject areas by applying the proposed methods to solve real-world problems in health science and social science. For example, in collaboration with world-leading researchers at the University of British Columbia's medical school, I will apply the methods in biomarker discovery for various lung diseases. The identified biomarkers can be used to develop diagnosis test chips or as targets for drug development. In collaboration with economists, I will apply the methods to the US stock market's high-frequency trading data and build a model to predict multiple stocks' prices. I believe such applications will lead to substantial impacts and contribute to the development of Canadian Natural Sciences and Engineering.