Open AccessEvaluating hierarchical machine learning approaches to classify biological databases
Author(s) -
Pâmela Marinho Rezende,
Joicymara Xavier,
David B. Ascher,
Gabriel R. Fernandes,
Douglas E V Pires
Publication year - 2022
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbac216
Subject(s) - computer science , hierarchy , task (project management) , data mining , machine learning , hierarchical database model , node (physics) , process (computing) , resource (disambiguation) , biological data , artificial intelligence , scale (ratio) , bioinformatics , computer network , physics , management , structural engineering , quantum mechanics , biology , economics , engineering , market economy , operating system
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 classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of 'Local per Level' and 'Local per Node' approaches with a 'Global' approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance.