1/2 of men and 1/3 of women will face cancer in their lifetime. Despite the growth in open-source whole-genome data and the advent of the $100/1-hour genome, genetic tests have remained limited to one to a few hundred genes, and the prognosis, diagnosis and treatment of cancer have remained unchanged. This is because of the lack of suitable AI/ML. Eigengene invents algorithms, known as comparative spectral decompositions, i.e., physics-inspired multi-tensor generalizations of the singular value decomposition, that connect the whole genome with a patient’s survival and response to treatment. Our algorithms discover connections that are clinically applicable to the population at large in whole genomes of 3B nucleotides each from cohorts as small as 50–100 patients. These accurate, precise, and interpretable connections outperform the best other indicators, where they exist. All other methods miss them. For example, our retrospective clinical trial validated a genome-wide pattern in tumors from glioblastoma brain cancer patients as the best predictor of life expectancy and response to standard of care. We discovered this, and predictors in, e.g., adult lung, ovarian, and uterine adenocarcinoma tumors and pediatric nerve neuroblastoma tumors, in public data, proving that the algorithms and predictors are uniquely suited to personalized medicine. Eigengene’s algorithms and predictors will help patients and oncologists manage cancer and disease far more effectively than what is currently possible. They can help clinical trials succeed by predicting which patients are likely to respond to a treatment. They can also help predict new drug targets and combinations of targets that are correlated with survival.