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Artigo de periodico
BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification (2024)
Santos, Anderson Paulo Avila ; Almeida, Breno Lívio Silva de ; Bonidia, Robson Parmezan ; Stadler, Peter F ; Štefanič, Polonca; Mandic-Mulec, Ines; Rocha, Ulisses Nunes da ; Sanches, Danilo Sipoli ; Carvalho, Andre Carlos Ponce de Leon Ferreira de
Fonte: RNA Biology. Unidades: ICMC, EESC E ICMC
Assuntos: BIOINFORMÁTICA, APRENDIZAGEM PROFUNDA, REDES NEURAIS, RNA, REGULAÇÃO GÊNICA, GENÔMICA
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ABNT
SANTOS, Anderson Paulo Avila et al. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, v. 21, n. 1, p. 1-12, 2024Tradução . . Disponível em: https://doi.org/10.1080/15476286.2024.2329451. Acesso em: 16 jun. 2024.
APA
Santos, A. P. A., Almeida, B. L. S. de, Bonidia, R. P., Stadler, P. F., Štefanič, P., Mandic-Mulec, I., et al. (2024). BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biology, 21( 1), 1-12. doi:10.1080/15476286.2024.2329451
NLM
Santos APA, Almeida BLS de, Bonidia RP, Stadler PF, Štefanič P, Mandic-Mulec I, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification [Internet]. RNA Biology. 2024 ; 21( 1): 1-12.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1080/15476286.2024.2329451
Vancouver
Santos APA, Almeida BLS de, Bonidia RP, Stadler PF, Štefanič P, Mandic-Mulec I, Rocha UN da, Sanches DS, Carvalho ACP de LF de. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification [Internet]. RNA Biology. 2024 ; 21( 1): 1-12.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1080/15476286.2024.2329451
Artigo de periodico
The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes (2023)
Santos, Anderson Paulo Avila ; Nata'ala, Muhammad Kabiru ; Kasmanas, Jonas Coelho ; Bartholomäus, Alexander ; Keller-Costa, Tina ; Jurburg, Stephanie D ; Tal, Tamara ; Camarinha-Silva, Amélia ; Saraiva, João Pedro ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Stadler, Peter F ; Sanches, Danilo Sipoli ; Rocha, Ulisses Nunes da
Fonte: Animal Microbiome. Unidade: ICMC
Assuntos: BIOINFORMÁTICA, GENÔMICA, ECOLOGIA MICROBIANA, METADADOS, BASES DE DADOS
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SANTOS, Anderson Paulo Avila et al. The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes. Animal Microbiome, v. 5, p. 1-13, 2023Tradução . . Disponível em: https://doi.org/10.1186/s42523-023-00267-3. Acesso em: 16 jun. 2024.
APA
Santos, A. P. A., Nata'ala, M. K., Kasmanas, J. C., Bartholomäus, A., Keller-Costa, T., Jurburg, S. D., et al. (2023). The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes. Animal Microbiome, 5, 1-13. doi:10.1186/s42523-023-00267-3
NLM
Santos APA, Nata'ala MK, Kasmanas JC, Bartholomäus A, Keller-Costa T, Jurburg SD, Tal T, Camarinha-Silva A, Saraiva JP, Carvalho ACP de LF de, Stadler PF, Sanches DS, Rocha UN da. The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes [Internet]. Animal Microbiome. 2023 ; 5 1-13.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s42523-023-00267-3
Vancouver
Santos APA, Nata'ala MK, Kasmanas JC, Bartholomäus A, Keller-Costa T, Jurburg SD, Tal T, Camarinha-Silva A, Saraiva JP, Carvalho ACP de LF de, Stadler PF, Sanches DS, Rocha UN da. The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes [Internet]. Animal Microbiome. 2023 ; 5 1-13.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s42523-023-00267-3
Artigo de periodico
Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy (2022)
Bonidia, Robson Parmezan ; Santos, Anderson Paulo Avila ; Almeida, Breno Lívio Silva de ; Stadler, Peter F ; Rocha, Ulisses Nunes da ; Sanches, Danilo Sipoli ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Entropy. Unidades: ICMC, EESC E ICMC
Assuntos: BIOINFORMÁTICA, SEQUENCIAMENTO GENÉTICO, TEORIA DA INFORMAÇÃO
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BONIDIA, Robson Parmezan et al. Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy. Entropy, v. 24, n. 10, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/e24101398. Acesso em: 16 jun. 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). Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy. Entropy, 24( 10), 1-17. doi:10.3390/e24101398
NLM
Bonidia RP, Santos APA, Almeida BLS de, Stadler PF, Rocha UN da, Sanches DS, Carvalho ACP de LF de. Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy [Internet]. Entropy. 2022 ; 24( 10): 1-17.[citado 2024 jun. 16 ] Available from: https://doi.org/10.3390/e24101398
Vancouver
Bonidia RP, Santos APA, Almeida BLS de, Stadler PF, Rocha UN da, Sanches DS, Carvalho ACP de LF de. Information theory for biological sequence classification: a novel feature extraction technique based on Tsallis entropy [Internet]. Entropy. 2022 ; 24( 10): 1-17.[citado 2024 jun. 16 ] Available from: https://doi.org/10.3390/e24101398
Artigo de periodico
MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes (2022)
Nata'ala, Muhammad Kabiru ; Santos, Anderson Paulo Avila ; Kasmanas, Jonas Coelho ; Bartholomäus, Alexander ; Saraiva, João Pedro ; Silva, Sandra Godinho ; Keller-Costa, Tina ; Costa, Rodrigo; Gomes, Newton Carlos Marcial ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Stadler, Peter F ; Sanches, Danilo Sipoli ; Rocha, Ulisses Nunes da
Fonte: Environmental Microbiome. Unidade: ICMC
Assuntos: BIOINFORMÁTICA, GENÔMICA, METADADOS, BANCO DE DADOS, ECOLOGIA MICROBIANA
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ABNT
NATA'ALA, Muhammad Kabiru et al. MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes. Environmental Microbiome, v. 17, p. 1-10, 2022Tradução . . Disponível em: https://doi.org/10.1186/s40793-022-00449-7. Acesso em: 16 jun. 2024.
APA
Nata'ala, M. K., Santos, A. P. A., Kasmanas, J. C., Bartholomäus, A., Saraiva, J. P., Silva, S. G., et al. (2022). MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes. Environmental Microbiome, 17, 1-10. doi:10.1186/s40793-022-00449-7
NLM
Nata'ala MK, Santos APA, Kasmanas JC, Bartholomäus A, Saraiva JP, Silva SG, Keller-Costa T, Costa R, Gomes NCM, Carvalho ACP de LF de, Stadler PF, Sanches DS, Rocha UN da. MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes [Internet]. Environmental Microbiome. 2022 ; 17 1-10.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s40793-022-00449-7
Vancouver
Nata'ala MK, Santos APA, Kasmanas JC, Bartholomäus A, Saraiva JP, Silva SG, Keller-Costa T, Costa R, Gomes NCM, Carvalho ACP de LF de, Stadler PF, Sanches DS, Rocha UN da. MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes [Internet]. Environmental Microbiome. 2022 ; 17 1-10.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s40793-022-00449-7
Artigo de periodico
Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture (2022)
Liu, Bin; Sträuber, Heike ; Saraiva, João Pedro ; Harms, Hauke ; Silva, Sandra Godinho ; Kasmanas, Jonas Coelho ; Kleinsteuber, Sabine ; Rocha, Ulisses Nunes da
Fonte: Microbiome. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, REATORES BIOQUÍMICOS, BIOINDICADORES
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ABNT
LIU, Bin et al. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. Microbiome, v. 10, p. 1-21, 2022Tradução . . Disponível em: https://doi.org/10.1186/s40168-021-01219-2. Acesso em: 16 jun. 2024.
APA
Liu, B., Sträuber, H., Saraiva, J. P., Harms, H., Silva, S. G., Kasmanas, J. C., et al. (2022). Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture. Microbiome, 10, 1-21. doi:10.1186/s40168-021-01219-2
NLM
Liu B, Sträuber H, Saraiva JP, Harms H, Silva SG, Kasmanas JC, Kleinsteuber S, Rocha UN da. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture [Internet]. Microbiome. 2022 ; 10 1-21.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s40168-021-01219-2
Vancouver
Liu B, Sträuber H, Saraiva JP, Harms H, Silva SG, Kasmanas JC, Kleinsteuber S, Rocha UN da. Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture [Internet]. Microbiome. 2022 ; 10 1-21.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1186/s40168-021-01219-2
Artigo de periodico
BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria (2022)
Bonidia, Robson Parmezan ; Santos, Anderson Paulo Avila ; Almeida, Breno Lívio Silva de; Stadler, Peter F ; Rocha, Ulisses Nunes da ; Sanches, Danilo Sipoli ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Briefings in Bioinformatics. Unidades: ICMC, EESC E ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA, SEQUENCIAMENTO GENÉTICO
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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: 16 jun. 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 jun. 16 ] 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 jun. 16 ] Available from: https://doi.org/10.1093/bib/bbac218
Trabalho de evento-anais periodico
Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches (2021)
Almeida, Breno Lívio Silva de; Queiroz, Alvaro Pedroso; Santos, Anderson Paulo Avila ; Bonidia, Robson Parmezan ; Rocha, Ulisses Nunes da ; Sanches, Danilo Sipoli ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidades: ICMC, EESC E ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA
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ABNT
ALMEIDA, Breno Lívio Silva de et al. Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91814-9_8. Acesso em: 16 jun. 2024. , 2021
APA
Almeida, B. L. S. de, Queiroz, A. P., Santos, A. P. A., Bonidia, R. P., Rocha, U. N. da, Sanches, D. S., & Carvalho, A. C. P. de L. F. de. (2021). Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-91814-9_8
NLM
Almeida BLS de, Queiroz AP, Santos APA, Bonidia RP, Rocha UN da, Sanches DS, Carvalho ACP de LF de. Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 81-92.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_8
Vancouver
Almeida BLS de, Queiroz AP, Santos APA, Bonidia RP, Rocha UN da, Sanches DS, Carvalho ACP de LF de. Feature importance analysis of non-coding DNA/RNA sequences based on machine learning approaches [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 81-92.[citado 2024 jun. 16 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_8
Artigo de periodico
OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes (2021)
Saraiva, João Pedro ; Bartholomäus, Alexander ; Kallies, René ; Gomes, Marta; Bicalho, Marcos; Kasmanas, Jonas Coelho ; Vogt, Carsten ; Chatzinotas, Antonis ; Stadler, Peter F ; Dias, Oscar ; Rocha, Ulisses Nunes da
Fonte: Life Science Alliance. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, GENÔMICA, ECOLOGIA MICROBIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, João Pedro et al. OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes. Life Science Alliance, v. 4, n. 12, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.26508/lsa.202101167. Acesso em: 16 jun. 2024.
APA
Saraiva, J. P., Bartholomäus, A., Kallies, R., Gomes, M., Bicalho, M., Kasmanas, J. C., et al. (2021). OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes. Life Science Alliance, 4( 12), 1-14. doi:10.26508/lsa.202101167
NLM
Saraiva JP, Bartholomäus A, Kallies R, Gomes M, Bicalho M, Kasmanas JC, Vogt C, Chatzinotas A, Stadler PF, Dias O, Rocha UN da. OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes [Internet]. Life Science Alliance. 2021 ; 4( 12): 1-14.[citado 2024 jun. 16 ] Available from: https://doi.org/10.26508/lsa.202101167
Vancouver
Saraiva JP, Bartholomäus A, Kallies R, Gomes M, Bicalho M, Kasmanas JC, Vogt C, Chatzinotas A, Stadler PF, Dias O, Rocha UN da. OrtSuite: from genomes to prediction of microbial interactions within targeted ecosystem processes [Internet]. Life Science Alliance. 2021 ; 4( 12): 1-14.[citado 2024 jun. 16 ] Available from: https://doi.org/10.26508/lsa.202101167

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