The dream of a powerful integrated computational framework, only hinted at in Ibn Sina's Canon, can now be fulfilled at a global scale as a result of many recent advances: foundational advances in statistical inference; hypothesis-driven experiment design and analysis and the dissemination of peer-reviewed publications among communities of scientists; distributed large-scale databases of scientific and auxiliary experimental data; algorithmic approaches to model building and model checking; machine learning approaches to generate large number of hypotheses, and multiple hypotheses testing to tame computational complexity and false-discovery rates, etc. We will focus on an application centered on cancer - "the emperor of all maladies." The topics this talk will cover include: Probabilistic causation, Causal analysis of Cancer genome data, Kernel based methods for survival analysis, Improved single-cell/single-molecule data via SubOptical Mapping, CRISPR Assays, CHA and Therapy design, Immuno-therapy, Liquid Biopsies, etc.