Fig. 1
From: Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis
Design modeling of heterogenous patient-specific healthcare, genomics, metabolomics, phenotypic, and lifestyle data, and publicly available annotation data including genes, variants, diseases, drugs, and biomarkers. Analysis using AI and ML approaches (Support Vector Machine, Deep Learning, Logistic Regression, Discrimination Analysis, Decision tree, Random Forest, Linear Regression, Naïve Bayes, K-Nearest Neighbor, Hidden Markov Model, and Genetic Algorithm), multifactor examination, knowledgebase and decision support system for data classification, cluster, and regression analysis. Furthermore, resource allocation for data storage and computational analysis