ReP USP - Resultado da busca

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: 28 nov. 2025. , 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 2025 nov. 28 ] 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 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_8
Trabalho de evento-anais periodico
MathPIP: classification of proinflammatory peptides using mathematical descriptors (2021)
Cavalcante, João Pedro Uchôa; Gonçalves, Anderson Cardoso; Bonidia, Robson Parmezan ; Sanches, Danilo Sipoli ; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA
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ABNT
CAVALCANTE, João Pedro Uchôa et al. MathPIP: classification of proinflammatory peptides using mathematical descriptors. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91814-9_13. Acesso em: 28 nov. 2025. , 2021
APA
Cavalcante, J. P. U., Gonçalves, A. C., Bonidia, R. P., Sanches, D. S., & Carvalho, A. C. P. de L. F. de. (2021). MathPIP: classification of proinflammatory peptides using mathematical descriptors. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-91814-9_13
NLM
Cavalcante JPU, Gonçalves AC, Bonidia RP, Sanches DS, Carvalho ACP de LF de. MathPIP: classification of proinflammatory peptides using mathematical descriptors [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 131-136.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_13
Vancouver
Cavalcante JPU, Gonçalves AC, Bonidia RP, Sanches DS, Carvalho ACP de LF de. MathPIP: classification of proinflammatory peptides using mathematical descriptors [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 131-136.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_13
Trabalho de evento-anais periodico
Topological characterization of cancer driver genes using reactome super pathways networks (2021)
Ramos, Rodrigo Henrique ; Cutigi, Jorge Francisco ; Ferreira, Cynthia de Oliveira Lage ; Simão, Adenilso da Silva
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assuntos: NEOPLASIAS, BIOINFORMÁTICA, REDES COMPLEXAS, TOPOLOGIA
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ABNT
RAMOS, Rodrigo Henrique et al. Topological characterization of cancer driver genes using reactome super pathways networks. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-91814-9_3. Acesso em: 28 nov. 2025. , 2021
APA
Ramos, R. H., Cutigi, J. F., Ferreira, C. de O. L., & Simão, A. da S. (2021). Topological characterization of cancer driver genes using reactome super pathways networks. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-91814-9_3
NLM
Ramos RH, Cutigi JF, Ferreira C de OL, Simão A da S. Topological characterization of cancer driver genes using reactome super pathways networks [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 26-37.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3
Vancouver
Ramos RH, Cutigi JF, Ferreira C de OL, Simão A da S. Topological characterization of cancer driver genes using reactome super pathways networks [Internet]. Lecture Notes in Bioinformatics. 2021 ; 13063 26-37.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-91814-9_3
Trabalho de evento-anais periodico
Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery (2020)
Cutigi, Jorge Francisco ; Evangelista, Renato Feijo ; Ramos, Rodrigo Henrique ; Ferreira, Cynthia de Oliveira Lage ; Evangelista, Adriane Feijó ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Simão, Adenilso da Silva
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assuntos: BIOINFORMÁTICA, REDES COMPLEXAS, APRENDIZADO COMPUTACIONAL, GENÔMICA, NEOPLASIAS
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ABNT
CUTIGI, Jorge Francisco et al. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-65775-8_8. Acesso em: 28 nov. 2025. , 2020
APA
Cutigi, J. F., Evangelista, R. F., Ramos, R. H., Ferreira, C. de O. L., Evangelista, A. F., Carvalho, A. C. P. de L. F. de, & Simão, A. da S. (2020). Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-65775-8_8
NLM
Cutigi JF, Evangelista RF, Ramos RH, Ferreira C de OL, Evangelista AF, Carvalho ACP de LF de, Simão A da S. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery [Internet]. Lecture Notes in Bioinformatics. 2020 ; 12558 81-92.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-65775-8_8
Vancouver
Cutigi JF, Evangelista RF, Ramos RH, Ferreira C de OL, Evangelista AF, Carvalho ACP de LF de, Simão A da S. Combining mutation and gene network data in a machine learning approach for false-positive cancer driver gene discovery [Internet]. Lecture Notes in Bioinformatics. 2020 ; 12558 81-92.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-65775-8_8
Trabalho de evento-anais periodico
'S POT.2' FS: single score feature selection applied to the problem of distinguishing long non-coding RNAs from protein coding transcripts (2018)
Kümmel, Bruno C; Carvalho, André Carlos Ponce de Leon Ferreira de ; Brigido, Marcelo M; Ralha, Célia Ghedini; Walter, Maria Emilia M. T.
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, BIOINFORMÁTICA
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ABNT
KÜMMEL, Bruno C et al. 'S POT.2' FS: single score feature selection applied to the problem of distinguishing long non-coding RNAs from protein coding transcripts. Lecture Notes in Bioinformatics. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-01722-4_10. Acesso em: 28 nov. 2025. , 2018
APA
Kümmel, B. C., Carvalho, A. C. P. de L. F. de, Brigido, M. M., Ralha, C. G., & Walter, M. E. M. T. (2018). 'S POT.2' FS: single score feature selection applied to the problem of distinguishing long non-coding RNAs from protein coding transcripts. Lecture Notes in Bioinformatics. Cham: Springer. doi:10.1007/978-3-030-01722-4_10
NLM
Kümmel BC, Carvalho ACP de LF de, Brigido MM, Ralha CG, Walter MEMT. 'S POT.2' FS: single score feature selection applied to the problem of distinguishing long non-coding RNAs from protein coding transcripts [Internet]. Lecture Notes in Bioinformatics. 2018 ; 11228 103-113.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-01722-4_10
Vancouver
Kümmel BC, Carvalho ACP de LF de, Brigido MM, Ralha CG, Walter MEMT. 'S POT.2' FS: single score feature selection applied to the problem of distinguishing long non-coding RNAs from protein coding transcripts [Internet]. Lecture Notes in Bioinformatics. 2018 ; 11228 103-113.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-030-01722-4_10
Trabalho de evento-anais periodico
Evaluating correlation coefficients for clustering gene expression profiles of cancer (2012)
Jaskowiak, Pablo A; Campello, Ricardo José Gabrielli Barreto; Costa, Ivan G
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics : Advances in Bioinformatics and Computational Biology - BSB. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto e COSTA, Ivan G. Evaluating correlation coefficients for clustering gene expression profiles of cancer. Lecture Notes in Bioinformatics. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-31927-3. Acesso em: 28 nov. 2025. , 2012
APA
Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2012). Evaluating correlation coefficients for clustering gene expression profiles of cancer. Lecture Notes in Bioinformatics. Berlin: Springer-Verlag. doi:10.1007/978-3-642-31927-3
NLM
Jaskowiak PA, Campello RJGB, Costa IG. Evaluating correlation coefficients for clustering gene expression profiles of cancer [Internet]. Lecture Notes in Bioinformatics. 2012 ; 7409 120-131.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-31927-3
Vancouver
Jaskowiak PA, Campello RJGB, Costa IG. Evaluating correlation coefficients for clustering gene expression profiles of cancer [Internet]. Lecture Notes in Bioinformatics. 2012 ; 7409 120-131.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-31927-3
Trabalho de evento-anais periodico
Efficiently querying protein sequences with the Proteinus index (2011)
Louza, Felipe Alves da; Ciferri, Ricardo Rodrigues; Ciferri, Cristina Dutra de Aguiar
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics : Advances in Bioinformatics and Computational Biology - BSB. Unidade: ICMC
Assunto: BANCO DE DADOS
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ABNT
LOUZA, Felipe Alves da e CIFERRI, Ricardo Rodrigues e CIFERRI, Cristina Dutra de Aguiar. Efficiently querying protein sequences with the Proteinus index. Lecture Notes in Bioinformatics. Heidelberg: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-22825-4_8. Acesso em: 28 nov. 2025. , 2011
APA
Louza, F. A. da, Ciferri, R. R., & Ciferri, C. D. de A. (2011). Efficiently querying protein sequences with the Proteinus index. Lecture Notes in Bioinformatics. Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-22825-4_8
NLM
Louza FA da, Ciferri RR, Ciferri CD de A. Efficiently querying protein sequences with the Proteinus index [Internet]. Lecture Notes in Bioinformatics. 2011 ; 6832 59-65.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-22825-4_8
Vancouver
Louza FA da, Ciferri RR, Ciferri CD de A. Efficiently querying protein sequences with the Proteinus index [Internet]. Lecture Notes in Bioinformatics. 2011 ; 6832 59-65.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-22825-4_8
Trabalho de evento-anais periodico
Hierarchical multilabel protein function prediction using local neural networks (2011)
Cerri, Ricardo; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics : Advances in Bioinformatics and Computational Biology - BSB. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
CERRI, Ricardo e CARVALHO, André Carlos Ponce de Leon Ferreira de. Hierarchical multilabel protein function prediction using local neural networks. Lecture Notes in Bioinformatics. Heidelberg: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-22825-4_2. Acesso em: 28 nov. 2025. , 2011
APA
Cerri, R., & Carvalho, A. C. P. de L. F. de. (2011). Hierarchical multilabel protein function prediction using local neural networks. Lecture Notes in Bioinformatics. Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-22825-4_2
NLM
Cerri R, Carvalho ACP de LF de. Hierarchical multilabel protein function prediction using local neural networks [Internet]. Lecture Notes in Bioinformatics. 2011 ; 6832 10-17.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-22825-4_2
Vancouver
Cerri R, Carvalho ACP de LF de. Hierarchical multilabel protein function prediction using local neural networks [Internet]. Lecture Notes in Bioinformatics. 2011 ; 6832 10-17.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1007/978-3-642-22825-4_2
Trabalho de evento-anais periodico
Comparing methods for multilabel classification of proteins using machine learning techniques (2009)
Cerri, Ricardo; Silva, Renato R. O. da; Carvalho, André Carlos Ponce de Leon Ferreira de
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
CERRI, Ricardo e SILVA, Renato R. O. da e CARVALHO, André Carlos Ponce de Leon Ferreira de. Comparing methods for multilabel classification of proteins using machine learning techniques. Lecture Notes in Bioinformatics. Heidelberg: Springer. Disponível em: http://www.springerlink.com/content/b95l0p8136463510/fulltext.pdf. Acesso em: 28 nov. 2025. , 2009
APA
Cerri, R., Silva, R. R. O. da, & Carvalho, A. C. P. de L. F. de. (2009). Comparing methods for multilabel classification of proteins using machine learning techniques. Lecture Notes in Bioinformatics. Heidelberg: Springer. Recuperado de http://www.springerlink.com/content/b95l0p8136463510/fulltext.pdf
NLM
Cerri R, Silva RRO da, Carvalho ACP de LF de. Comparing methods for multilabel classification of proteins using machine learning techniques [Internet]. Lecture Notes in Bioinformatics. 2009 ; 5676 109-120.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/b95l0p8136463510/fulltext.pdf
Vancouver
Cerri R, Silva RRO da, Carvalho ACP de LF de. Comparing methods for multilabel classification of proteins using machine learning techniques [Internet]. Lecture Notes in Bioinformatics. 2009 ; 5676 109-120.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/b95l0p8136463510/fulltext.pdf
Trabalho de evento-anais periodico
Representations for evolutionary algorithms applied to protein structure prediction problem using HP model (2009)
Gabriel, Paulo H. R.; Delbem, Alexandre Cláudio Botazzo
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB. Unidade: ICMC
Assuntos: SISTEMAS EMBUTIDOS, COMPUTAÇÃO EVOLUTIVA
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ABNT
GABRIEL, Paulo H. R. e DELBEM, Alexandre Cláudio Botazzo. Representations for evolutionary algorithms applied to protein structure prediction problem using HP model. Lecture Notes in Bioinformatics. Heidelberg: Springer. Disponível em: http://www.springerlink.com/content/37t56412q3108837/fulltext.pdf. Acesso em: 28 nov. 2025. , 2009
APA
Gabriel, P. H. R., & Delbem, A. C. B. (2009). Representations for evolutionary algorithms applied to protein structure prediction problem using HP model. Lecture Notes in Bioinformatics. Heidelberg: Springer. Recuperado de http://www.springerlink.com/content/37t56412q3108837/fulltext.pdf
NLM
Gabriel PHR, Delbem ACB. Representations for evolutionary algorithms applied to protein structure prediction problem using HP model [Internet]. Lecture Notes in Bioinformatics. 2009 ; 5676 97-108.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/37t56412q3108837/fulltext.pdf
Vancouver
Gabriel PHR, Delbem ACB. Representations for evolutionary algorithms applied to protein structure prediction problem using HP model [Internet]. Lecture Notes in Bioinformatics. 2009 ; 5676 97-108.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/37t56412q3108837/fulltext.pdf
Trabalho de evento-anais periodico
Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions (2008)
Costa, Eduardo P.; Lorena, Ana Carolina; Carvalho, André Carlos Ponce de Leon Ferreira de ; Freitas, Alex A.
Fonte: Lecture Notes in Bioinformatics. Nome do evento: Brazilian Symposium on Bioinformatics - BSB 2008. Unidade: ICMC
Assuntos: BIOINFORMÁTICA, COMPUTAÇÃO BIOINSPIRADA
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ABNT
COSTA, Eduardo P. et al. Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions. Lecture Notes in Bioinformatics. Berlin: Springer. Disponível em: http://www.springerlink.com/content/u0530m52723v/?p=f41f3d06b37f495698fa200b3e7f1666&pi=91. Acesso em: 28 nov. 2025. , 2008
APA
Costa, E. P., Lorena, A. C., Carvalho, A. C. P. de L. F. de, & Freitas, A. A. (2008). Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions. Lecture Notes in Bioinformatics. Berlin: Springer. Recuperado de http://www.springerlink.com/content/u0530m52723v/?p=f41f3d06b37f495698fa200b3e7f1666&pi=91
NLM
Costa EP, Lorena AC, Carvalho ACP de LF de, Freitas AA. Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions [Internet]. Lecture Notes in Bioinformatics. 2008 ; 5167 35-46.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/u0530m52723v/?p=f41f3d06b37f495698fa200b3e7f1666&pi=91
Vancouver
Costa EP, Lorena AC, Carvalho ACP de LF de, Freitas AA. Top-down hierarchical ensembles of classifiers for predicting G-protein-coupled-receptor functions [Internet]. Lecture Notes in Bioinformatics. 2008 ; 5167 35-46.[citado 2025 nov. 28 ] Available from: http://www.springerlink.com/content/u0530m52723v/?p=f41f3d06b37f495698fa200b3e7f1666&pi=91

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