The overall goals of our project are to develop statistical and bioinformatics methodologies for analyses of high-throughput omics technologies, and apply these methods to prediction problems of common diseases such as cancer. We will develop model-based and computationally intensive methods for integrated processing, analysis and interpretation of multiple omics data. In this work, we consider the disease prediction problem in a very broad sense, ranging from raw data pre-processing and identification of relevant biomarkers to prediction of survival or response to therapy. Integration of complementary information from multiple levels of 'omics' data, including the genome, transcriptome, spatial transcriptomics, metagenomics and pharmacogenomics, can greatly facilitate the discoveries of true causes/drivers of a disease and its response to therapy. I have been already part of the current projects NAISS 2024/5-111, NAISS 2024/6-196 and sens2018116 as a co-Investigator. We recently focus on advanced machine learning methods including deep learning methods, generative AI methods such as large language models to advance our research, which require resources of GPU computing. This will include 1) analysis spatial transcriptomics data generated from new technologies such as 10X Genomics Visium which contains both high-throughput sequencing data and image data, 2) analysis of huge public and in-house data including biomedical literature text, disease and drug information, molecular knowledge, etc in pharmacogenomics research.