Evaluating hierarchical machine learning approaches to classify biological databases.

ABSTRACT: The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include 'Local' approaches considering the hierarchy, building models per level or node, and 'Global' hierarchical

SUBMITTER: Rezende PM 

PROVIDER: S-EPMC9310517 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature