Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets.

ABSTRACT: Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-

SUBMITTER: Marino S 

PROVIDER: S-EPMC7455041 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature