BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria (2022)
- Authors:
- USP affiliated authors: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; ALMEIDA, BRENO LIVIO SILVA DE - EESC E ICMC ; BONIDIA, ROBSON PARMEZAN - ICMC ; SANTOS, ANDERSON PAULO AVILA - ICMC
- Unidades: ICMC; EESC E ICMC
- DOI: 10.1093/bib/bbac218
- Subjects: APRENDIZADO COMPUTACIONAL; BIOINFORMÁTICA; SEQUENCIAMENTO GENÉTICO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Briefings in Bioinformatics
- ISSN: 1467-5463
- Volume/Número/Paginação/Ano: v. 23, n. 4, p. 1-13, 2022
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by-nc
-
ABNT
BONIDIA, Robson Parmezan et al. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. Briefings in Bioinformatics, v. 23, n. 4, p. 1-13, 2022Tradução . . Disponível em: https://doi.org/10.1093/bib/bbac218. Acesso em: 24 abr. 2024. -
APA
Bonidia, R. P., Santos, A. P. A., Almeida, B. L. S. de, Stadler, P. F., Rocha, U. N. da, Sanches, D. S., & Carvalho, A. C. P. de L. F. de. (2022). BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. Briefings in Bioinformatics, 23( 4), 1-13. doi:10.1093/bib/bbac218 -
NLM
Bonidia RP, Santos APA, Almeida BLS de, Stadler PF, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria [Internet]. Briefings in Bioinformatics. 2022 ; 23( 4): 1-13.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1093/bib/bbac218 -
Vancouver
Bonidia RP, Santos APA, Almeida BLS de, Stadler PF, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria [Internet]. Briefings in Bioinformatics. 2022 ; 23( 4): 1-13.[citado 2024 abr. 24 ] Available from: https://doi.org/10.1093/bib/bbac218 - BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification
- Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy
- Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches
- A novel decomposing model with evolutionary algorithms for feature selection in long non-coding RNAs
- Feature extraction approaches for biological sequences: a comparative study of mathematical features
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical
- The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes
- MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes
- CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems
- BioAutoML: Democratizing Machine Learning in Life Sciences
Informações sobre o DOI: 10.1093/bib/bbac218 (Fonte: oaDOI API)
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