BioPrediction: democratizing machine learning in the study of molecular interactions (2023)
- Authors:
- USP affiliated authors: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC ; FLORENTINO, BRUNO RAFAEL - IFSC ; SANCHES, NATAN HENRIQUE - ICMC ; BONIDIA, ROBSON PARMEZAN - ICMC
- Unidades: ICMC; IFSC
- DOI: 10.5753/eniac.2023.234271
- Subjects: APRENDIZADO COMPUTACIONAL; BIOINFORMÁTICA; SEQUENCIAMENTO GENÉTICO
- Keywords: Molecular Interactions; Democratizing Machine Learning; Biological Sequences
- Language: Português
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2023
- Source:
- Conference titles: Encontro Nacional de Inteligência Artificial e Computacional - ENIAC
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: bronze
-
ABNT
FLORENTINO, Bruno Rafael et al. BioPrediction: democratizing machine learning in the study of molecular interactions. 2023, Anais.. Porto Alegre: SBC, 2023. Disponível em: https://doi.org/10.5753/eniac.2023.234271. Acesso em: 03 maio 2024. -
APA
Florentino, B. R., Sanches, N. H., Bonidia, R. P., & Carvalho, A. C. P. de L. F. de. (2023). BioPrediction: democratizing machine learning in the study of molecular interactions. In Anais. Porto Alegre: SBC. doi:10.5753/eniac.2023.234271 -
NLM
Florentino BR, Sanches NH, Bonidia RP, Carvalho ACP de LF de. BioPrediction: democratizing machine learning in the study of molecular interactions [Internet]. Anais. 2023 ;[citado 2024 maio 03 ] Available from: https://doi.org/10.5753/eniac.2023.234271 -
Vancouver
Florentino BR, Sanches NH, Bonidia RP, Carvalho ACP de LF de. BioPrediction: democratizing machine learning in the study of molecular interactions [Internet]. Anais. 2023 ;[citado 2024 maio 03 ] Available from: https://doi.org/10.5753/eniac.2023.234271 - BioPrediction: democratizando a aprendizagem de máquina no estudo de interações moleculares
- MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical
- 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
- CRISPRloci: comprehensive and accurate annotation of CRISPR-Cas systems
- Pilot sequence allocation schemes in massive MIMO systems using heuristic approaches
- BioAutoML: Democratizing Machine Learning in Life Sciences
- BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
- 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
Informações sobre o DOI: 10.5753/eniac.2023.234271 (Fonte: oaDOI API)
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