Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm - Chalise, Prabhakar; Fridley, Brooke L. (PloS one, 2017)
Keywords: Abnormalities; Algorithms; Antibodies; Applications of mathematics; Assaying; Bayesian analysis; Bioinformatics; Biological activity; Biology; Biology and life sciences; Biometrics; Brain cancer; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Cancer; Classification; Cluster Analysis; Clusters; Coefficients; Computational Biology - methods; Computer applications; Computer graphics; Computer programs; Computer Simulation; Constraints; Convergence; Data processing; Databases, Genetic; Deoxyribonucleic acid; DNA; DNA Copy Number Variations; DNA methylation; DNA Methylation - physiology; DNA microarrays; Epidermal growth factor receptors; Estimation; Etiology; Factorization; Fruits; Gene expression; Gene Expression - physiology; Genomes; Genomic analysis; Genomics; Glioblastoma; Glioblastoma - classification; Glioblastoma - genetics; Glioblastoma - metabolism; Humans; Intelligence; Jaw; Machine learning; Mathematical models; Matrices (Mathematics); Medicine and Health Sciences; Methods; MicroRNAs - metabolism; Nonlinear programming; Objective function; Ovarian cancer; Ovarian carcinoma; Pharmacology; Physical characteristics; Physical Sciences; Probability distribution; Research and Analysis Methods; RNA, Messenger - metabolism; Simulation; Software; Statistical analysis; Statistical distributions; Tumors; United States--US; Kansas City Missouri