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Data-driven personalized or precision medicine is coming of age. It's increasingly easy to collect relevant "big data" from genomics to clinics. However, the interpretation of "big data" and translation into down-stream testable experimental hypotheses and patient-beneficial clinical applications or therapeutics remain difficult. This book demonstrates how to leverage and integrate computational & systems biology analysis of cancer genomics data and genome-wide functional genomics screens to identify novel driver-type therapeutic targets as well as predictive biomarkers to overcome drug-resistance and to treat aggressive human cancers. Specifically the author developed network-based systems biology framework to infer disease drivers from large volumes of cancer genomics data and sophisticated statistical algorithms to deconvolute and analyze noisy high-throughput microarray or NGS-based functional genomics screening data. Two of the discoveries from this book have successfully launched two clinical trials and one biomarker patent for specific subtypes of breast cancer.