Evaluating hierarchical machine learning approaches to classify biological databases.

Saved in:
Bibliographic Details
Title: Evaluating hierarchical machine learning approaches to classify biological databases.
Authors: Rezende PM; Universidade Federal de Minas Gerais.; Instituto René Rachou, Fundação Oswaldo Cruz.; Stilingue Inteligência Artificial., Xavier JS; Universidade Federal de Minas Gerais.; Instituto René Rachou, Fundação Oswaldo Cruz.; Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri., Ascher DB; School of Chemistry and Molecular Biosciences, University of Queensland.; Systems and Computational Biology, Bio 21 Institute, University of Melbourne.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute., Fernandes GR; Instituto René Rachou, Fundação Oswaldo Cruz., Pires DEV; Systems and Computational Biology, Bio 21 Institute, University of Melbourne.; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute.; School of Computing and Information Systems, University of Melbourne.
Source: Briefings in bioinformatics [Brief Bioinform] 2022 Jul 18; Vol. 23 (4).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
Description
ISSN:1477-4054
DOI:10.1093/bib/bbac216