With the advent of next-generation sequencing (NGS) and other high-throughput omics technologies, systems integration is becoming the driving force for 21st century biology and medicine. To fully realize the value of such genome-scale data for knowledge discovery and disease understanding requires advanced bioinformatics for integration, mining, comparative analysis, and functional interpretation. We have developed a bioinformatics research infrastructure that integrates disparate databases and text mining tools in an ontological framework for automatic construction of knowledge networks and visual analysis of omics data. Our natural language processing (NLP) framework supports full-scale literature mining and generalizable relation extraction to connect gene/protein, mutation, miRNA to drug, disease and phenotype in personalized medicine context. This talk will highlight our collaborative projects with large-scale national initiatives, including the NIH LINCS-BD2K (Big Data to Knowledge) and TCGA/CPTAC cancer consortium projects to understand the impact of kinase inhibitor drugs on signaling pathways in cancer therapy.