Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.

ABSTRACT: The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elu

SUBMITTER: Jafari M 

PROVIDER: S-EPMC7479204 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature