Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis

Antecedentes: la estenosis valvular aórtica calcificada (CAVS) es una enfermedad mortal y no existe un tratamiento farmacológico para prevenir la progresión de la CAVS. Este estudio tiene como objetivo identificar genes potencialmente implicados con CAVS en pacientes con válvula aórtica bicúspide co...

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Autores:: Guauque Olarte, Sandra
Cantor, Erika
Rosas, Harvey
Salas, Rodrigo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

Idioma:

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repository_id_str
dc.title.spa.fl_str_mv	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
title	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
spellingShingle	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis enfermedad de la válvula aórtica calcificada selección de genes Aprendizaje automático Conocimiento previo Calcific aortic valve disease Gene-selection Machine learning Prior-knowledge
title_short	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
title_full	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
title_fullStr	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
title_full_unstemmed	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
title_sort	Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis
dc.creator.fl_str_mv	Guauque Olarte, Sandra Cantor, Erika Rosas, Harvey Salas, Rodrigo
dc.contributor.author.none.fl_str_mv	Guauque Olarte, Sandra Cantor, Erika Rosas, Harvey Salas, Rodrigo
dc.subject.spa.fl_str_mv	enfermedad de la válvula aórtica calcificada selección de genes Aprendizaje automático Conocimiento previo
topic	enfermedad de la válvula aórtica calcificada selección de genes Aprendizaje automático Conocimiento previo Calcific aortic valve disease Gene-selection Machine learning Prior-knowledge
dc.subject.other.spa.fl_str_mv	Calcific aortic valve disease Gene-selection Machine learning Prior-knowledge
description	Antecedentes: la estenosis valvular aórtica calcificada (CAVS) es una enfermedad mortal y no existe un tratamiento farmacológico para prevenir la progresión de la CAVS. Este estudio tiene como objetivo identificar genes potencialmente implicados con CAVS en pacientes con válvula aórtica bicúspide congénita (BAV) y válvula aórtica tricúspide (TAV) en comparación con pacientes que tienen válvulas normales, utilizando un bosque aleatorio inclinado (RF) de conocimiento. Resultados: Este estudio implementó un bosque aleatorio inclinado de conocimiento (RF) utilizando información extraída de una red de interacciones proteína-proteína para clasificar genes con el fin de modificar su probabilidad de selección para dibujar las variables divididas candidatas. Se evaluaron un total de 15.191 genes en 19 válvulas con CAVS (BAV, n = 10; TAV, n = 9) y 8 válvulas normales. El desempeño del modelo se evaluó usando precisión, sensibilidad y especificidad para discriminar casos con CAVS. También se realizó una comparación con RF convencional. El rendimiento de este enfoque propuesto informó una precisión mejorada en comparación con la RF convencional para clasificar los casos por separado con BAV y TAV (RF inclinada: 59,3% frente a 40,7%). Cuando los pacientes con BAV y TAV se agruparon contra pacientes con válvulas normales, la adición de información biológica previa no fue relevante con una precisión del 92,6%. Conclusión: El enfoque de RF basado en el conocimiento reflejó el conocimiento biológico previo, lo que llevó a una mejor precisión para distinguir entre casos con BAV, TAV y válvulas normales. Los resultados de este estudio sugieren que la integración del conocimiento biológico puede ser útil durante tareas de clasificación difíciles.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-07-23
dc.date.accessioned.none.fl_str_mv	2022-10-14T00:05:11Z
dc.date.available.none.fl_str_mv	2022-10-14T00:05:11Z
dc.type.none.fl_str_mv	Artículos Científicos
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dc.identifier.issn.spa.fl_str_mv	1756-0381 (Electronic)
dc.identifier.uri.spa.fl_str_mv	https://doi.org/10.1186/s13040-021-00269-4
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dc.identifier.bibliographicCitation.spa.fl_str_mv	Cantor, E., Salas, R., Rosas, H. et al. (2021). Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis. BioData Mining 14, 35 (2021). https://doi.org/10.1186/s13040-021-00269-4.https://repository.ucc.edu.co/handle/20.500.12494/46729
identifier_str_mv	1756-0381 (Electronic) Cantor, E., Salas, R., Rosas, H. et al. (2021). Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis. BioData Mining 14, 35 (2021). https://doi.org/10.1186/s13040-021-00269-4.https://repository.ucc.edu.co/handle/20.500.12494/46729
url	https://doi.org/10.1186/s13040-021-00269-4 https://hdl.handle.net/20.500.12494/46729
dc.relation.isversionof.spa.fl_str_mv	https://biodatamining.biomedcentral.com/articles/10.1186/s13040-021-00269-4
dc.relation.ispartofjournal.spa.fl_str_mv	BioData mining
dc.relation.references.spa.fl_str_mv	I. Mordi, N. Tzemos, Bicuspid aortic valve disease: a comprehensive review., Cardiol. Res. Pract. 2012 (2012) 196037. R.L.J. Osnabrugge, D. Mylotte, S.J. Head, N.M. Van Mieghem, V.T. Nkomo, C.M. LeReun, A.J.J.C. Bogers, N. Piazza, A.P. Kappetein, Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study., J. Am. Coll. Cardiol. 62 (2013) 1002–1012. B. Alushi, L. Curini, M.R. Christopher, H. Grubitzch, U. Landmesser, A. Amedei, A. Lauten, Calcific Aortic Valve Disease-Natural History and Future Therapeutic Strategies., Front. Pharmacol. 11 (2020) 685. W.V. Li, J.J. Li, Modeling and analysis of RNA-seq data: a review from a statistical perspective, Quant. Biol. 6 (2018) 195–209. C. Wang, J.L. Gevertz, Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches, Stat. Appl. Genet. Mol. Biol. 15 (2016) 321–347. B. Efron, Prediction, Estimation, and Attribution, J. Am. Stat. Assoc. 115 (2020) 636–655. R. Couronné, P. Probst, A.-L. Boulesteix, Random forest versus logistic regression: a large-scale benchmark experiment, BMC Bioinformatics. 19 (2018) 270. J.A. Nepomuceno, A. Troncoso, I.A. Nepomuceno-Chamorro, J.S. Aguilar-Ruiz, Integrating biological knowledge based on functional annotations for biclustering of gene expression data, Comput. Methods Programs Biomed. 119 (2015) 163–180. A. Oskooei, M. Manica, R. Mathis, M.R. Martínez, Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer., Sci. Rep. 9 (2019) 15918. J. Crawford, C.S. Greene, Incorporating biological structure into machine learning models in biomedicine, Curr. Opin. Biotechnol. 63 (2020) 126–134. X. Guan, G. Runger, L. Liu, Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery, BMC Bioinformatics. 21 (2020) 77. S. Guauque-Olarte, A. Droit, J. Tremblay-Marchand, N. Gaudreault, D. Kalavrouziotis, F. Dagenais, J.G. Seidman, S.C. Body, P. Pibarot, P. Mathieu, Y. Bossé, RNA expression profile of calcified bicuspid, tricuspid, and normal human aortic valves by RNA sequencing., Physiol. Genomics. 48 (2016) 749–761. J. Zhang, J. Yang, T. Huang, Y. Shu, L. Chen, Identification of novel proliferative diabetic retinopathy related genes on protein-protein interaction network, Neurocomputing. 217 (2016) 63–72. D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K.P. Tsafou, M. Kuhn, P. Bork, L.J. Jensen, C. von Mering, STRING v10: protein-protein interaction networks, integrated over the tree of life., Nucleic Acids Res. 43 (2015) D447-52. S. Köhler, S. Bauer, D. Horn, P.N. Robinson, Walking the interactome for prioritization of candidate disease genes., Am. J. Hum. Genet. 82 (2008) 949–958. R. Padang, R.D. Bagnall, T. Tsoutsman, P.G. Bannon, C. Semsarian, Comparative transcriptome profiling in human bicuspid aortic valve disease using RNA sequencing., Physiol. Genomics. 47 (2015) 75–87. M. Hofree, J.P. Shen, H. Carter, A. Gross, T. Ideker, Network-based stratification of tumor mutations, Nat. Methods. 10 (2013) 1108–1115. L. Breiman, Random forests, Mach. Learn. 45 (2001) 5–32. L. Van der Maaten, G. Hinton, Visualizing data using t-SNE., J. Mach. Learn. Res. 9 (2008) 2579–2605. M.N. Wright, A. Ziegler, ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw. 77 (2017) 1–17. M. Kuhn, Building Predictive Models in R Using the caret Package, J. Stat. Softw. 28 (2008) 1–26. C.-H. Sia, J.S.-Y. Ho, J.J.-L. Chua, B.Y.-Q. Tan, N.J. Ngiam, N. Chew, H.-W. Sim, R. Chen, C.-H. Lee, T.-C. Yeo, W.K.-F. Kong, K.-K. Poh, Comparison of Clinical and Echocardiographic Features of Asymptomatic Patients With Stenotic Bicuspid Versus Tricuspid Aortic Valves, Am. J. Cardiol. 128 (2020) 210–215. M.A. Heuschkel, N.T. Skenteris, J.D. Hutcheson, D.D. van der Valk, J. Bremer, P. Goody, J. Hjortnaes, F. Jansen, C.V.C. Bouten, A. van den Bogaerdt, L. Matic, N. Marx, C. Goettsch, Integrative Multi-Omics Analysis in Calcific Aortic Valve Disease Reveals a Link to the Formation of Amyloid-Like Deposits., Cells. 9 (2020). J.L. Speiser, M.E. Miller, J. Tooze, E. Ip, A comparison of random forest variable selection methods for classification prediction modeling, Expert Syst. Appl. 134 (2019) 93–101. A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, M. Lang, Benchmark for filter methods for feature selection in high-dimensional classification data, Comput. Stat. Data Anal. 143 (2020) 106839.
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spelling	Guauque Olarte, SandraCantor, ErikaRosas, HarveySalas, Rodrigo14(1)2022-10-14T00:05:11Z2022-10-14T00:05:11Z2021-07-231756-0381 (Electronic)https://doi.org/10.1186/s13040-021-00269-4https://hdl.handle.net/20.500.12494/46729Cantor, E., Salas, R., Rosas, H. et al. (2021). Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis. BioData Mining 14, 35 (2021). https://doi.org/10.1186/s13040-021-00269-4.https://repository.ucc.edu.co/handle/20.500.12494/46729Antecedentes: la estenosis valvular aórtica calcificada (CAVS) es una enfermedad mortal y no existe un tratamiento farmacológico para prevenir la progresión de la CAVS. Este estudio tiene como objetivo identificar genes potencialmente implicados con CAVS en pacientes con válvula aórtica bicúspide congénita (BAV) y válvula aórtica tricúspide (TAV) en comparación con pacientes que tienen válvulas normales, utilizando un bosque aleatorio inclinado (RF) de conocimiento. Resultados: Este estudio implementó un bosque aleatorio inclinado de conocimiento (RF) utilizando información extraída de una red de interacciones proteína-proteína para clasificar genes con el fin de modificar su probabilidad de selección para dibujar las variables divididas candidatas. Se evaluaron un total de 15.191 genes en 19 válvulas con CAVS (BAV, n = 10; TAV, n = 9) y 8 válvulas normales. El desempeño del modelo se evaluó usando precisión, sensibilidad y especificidad para discriminar casos con CAVS. También se realizó una comparación con RF convencional. El rendimiento de este enfoque propuesto informó una precisión mejorada en comparación con la RF convencional para clasificar los casos por separado con BAV y TAV (RF inclinada: 59,3% frente a 40,7%). Cuando los pacientes con BAV y TAV se agruparon contra pacientes con válvulas normales, la adición de información biológica previa no fue relevante con una precisión del 92,6%. Conclusión: El enfoque de RF basado en el conocimiento reflejó el conocimiento biológico previo, lo que llevó a una mejor precisión para distinguir entre casos con BAV, TAV y válvulas normales. Los resultados de este estudio sugieren que la integración del conocimiento biológico puede ser útil durante tareas de clasificación difíciles.Background: Calcific aortic valve stenosis (CAVS) is a fatal disease and there is no pharmacological treatment to prevent the progression of CAVS. This study aims to identify genes potentially involved with CAVS in patients with congenital bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV) compared to patients who have normal valves, using a tilted random forest (RF) of knowledge.https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=00005140120000-0003-0336-9682https://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005649sandra.guauque@campusucc.edu.cohttps://scholar.google.ca/citations?user=9uoINksAAAAJ&hl=en35Universidad Cooperativa de Colombia, Facultad de Ciencias de la Salud, Odontología, Medellín y EnvigadoOdontologíaMedellínhttps://biodatamining.biomedcentral.com/articles/10.1186/s13040-021-00269-4BioData miningI. Mordi, N. Tzemos, Bicuspid aortic valve disease: a comprehensive review., Cardiol. Res. Pract. 2012 (2012) 196037.R.L.J. Osnabrugge, D. Mylotte, S.J. Head, N.M. Van Mieghem, V.T. Nkomo, C.M. LeReun, A.J.J.C. Bogers, N. Piazza, A.P. Kappetein, Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study., J. Am. Coll. Cardiol. 62 (2013) 1002–1012.B. Alushi, L. Curini, M.R. Christopher, H. Grubitzch, U. Landmesser, A. Amedei, A. Lauten, Calcific Aortic Valve Disease-Natural History and Future Therapeutic Strategies., Front. Pharmacol. 11 (2020) 685.W.V. Li, J.J. Li, Modeling and analysis of RNA-seq data: a review from a statistical perspective, Quant. Biol. 6 (2018) 195–209.C. Wang, J.L. Gevertz, Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches, Stat. Appl. Genet. Mol. Biol. 15 (2016) 321–347.B. Efron, Prediction, Estimation, and Attribution, J. Am. Stat. Assoc. 115 (2020) 636–655.R. Couronné, P. Probst, A.-L. Boulesteix, Random forest versus logistic regression: a large-scale benchmark experiment, BMC Bioinformatics. 19 (2018) 270.J.A. Nepomuceno, A. Troncoso, I.A. Nepomuceno-Chamorro, J.S. Aguilar-Ruiz, Integrating biological knowledge based on functional annotations for biclustering of gene expression data, Comput. Methods Programs Biomed. 119 (2015) 163–180.A. Oskooei, M. Manica, R. Mathis, M.R. Martínez, Network-based Biased Tree Ensembles (NetBiTE) for Drug Sensitivity Prediction and Drug Sensitivity Biomarker Identification in Cancer., Sci. Rep. 9 (2019) 15918.J. Crawford, C.S. Greene, Incorporating biological structure into machine learning models in biomedicine, Curr. Opin. Biotechnol. 63 (2020) 126–134.X. Guan, G. Runger, L. Liu, Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery, BMC Bioinformatics. 21 (2020) 77.S. Guauque-Olarte, A. Droit, J. Tremblay-Marchand, N. Gaudreault, D. Kalavrouziotis, F. Dagenais, J.G. Seidman, S.C. Body, P. Pibarot, P. Mathieu, Y. Bossé, RNA expression profile of calcified bicuspid, tricuspid, and normal human aortic valves by RNA sequencing., Physiol. Genomics. 48 (2016) 749–761.J. Zhang, J. Yang, T. Huang, Y. Shu, L. Chen, Identification of novel proliferative diabetic retinopathy related genes on protein-protein interaction network, Neurocomputing. 217 (2016) 63–72.D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K.P. Tsafou, M. Kuhn, P. Bork, L.J. Jensen, C. von Mering, STRING v10: protein-protein interaction networks, integrated over the tree of life., Nucleic Acids Res. 43 (2015) D447-52.S. Köhler, S. Bauer, D. Horn, P.N. Robinson, Walking the interactome for prioritization of candidate disease genes., Am. J. Hum. Genet. 82 (2008) 949–958.R. Padang, R.D. Bagnall, T. Tsoutsman, P.G. Bannon, C. Semsarian, Comparative transcriptome profiling in human bicuspid aortic valve disease using RNA sequencing., Physiol. Genomics. 47 (2015) 75–87.M. Hofree, J.P. Shen, H. Carter, A. Gross, T. Ideker, Network-based stratification of tumor mutations, Nat. Methods. 10 (2013) 1108–1115.L. Breiman, Random forests, Mach. Learn. 45 (2001) 5–32.L. Van der Maaten, G. Hinton, Visualizing data using t-SNE., J. Mach. Learn. Res. 9 (2008) 2579–2605.M.N. Wright, A. Ziegler, ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R, J. Stat. Softw. 77 (2017) 1–17.M. Kuhn, Building Predictive Models in R Using the caret Package, J. Stat. Softw. 28 (2008) 1–26.C.-H. Sia, J.S.-Y. Ho, J.J.-L. Chua, B.Y.-Q. Tan, N.J. Ngiam, N. Chew, H.-W. Sim, R. Chen, C.-H. Lee, T.-C. Yeo, W.K.-F. Kong, K.-K. Poh, Comparison of Clinical and Echocardiographic Features of Asymptomatic Patients With Stenotic Bicuspid Versus Tricuspid Aortic Valves, Am. J. Cardiol. 128 (2020) 210–215.M.A. Heuschkel, N.T. Skenteris, J.D. Hutcheson, D.D. van der Valk, J. Bremer, P. Goody, J. Hjortnaes, F. Jansen, C.V.C. Bouten, A. van den Bogaerdt, L. Matic, N. Marx, C. Goettsch, Integrative Multi-Omics Analysis in Calcific Aortic Valve Disease Reveals a Link to the Formation of Amyloid-Like Deposits., Cells. 9 (2020).J.L. Speiser, M.E. Miller, J. Tooze, E. Ip, A comparison of random forest variable selection methods for classification prediction modeling, Expert Syst. Appl. 134 (2019) 93–101.A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, M. Lang, Benchmark for filter methods for feature selection in high-dimensional classification data, Comput. Stat. Data Anal. 143 (2020) 106839.enfermedad de la válvula aórtica calcificadaselección de genesAprendizaje automáticoConocimiento previoCalcific aortic valve diseaseGene-selectionMachine learningPrior-knowledgeBiological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosisArtículos Científicoshttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionAtribucióninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2PublicationORIGINALBiodata mining_Guauque._Repositorio.pdfBiodata mining_Guauque._Repositorio.pdfapplication/pdf527928https://repository.ucc.edu.co/bitstreams/dcec8952-ce66-40ac-a58f-532291f2a79c/downloadec5ea3cc7ea34474aa64c8b13e072168MD51BioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdfBioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdfLicencia de usoapplication/pdf195587https://repository.ucc.edu.co/bitstreams/5d292e60-6a72-4a0c-a7fa-acc9c5f554a7/download466edda6de7e6c61a298b1962ffaf198MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repository.ucc.edu.co/bitstreams/15462b5f-245b-4153-895f-f6e5b992a5a6/download8a4605be74aa9ea9d79846c1fba20a33MD54THUMBNAILBiodata mining_Guauque._Repositorio.pdf.jpgBiodata mining_Guauque._Repositorio.pdf.jpgGenerated Thumbnailimage/jpeg2854https://repository.ucc.edu.co/bitstreams/2d63d6d4-2211-4f8a-8bf7-55e8f979a942/download0332802e91cb932d5af29b77107c3428MD55BioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdf.jpgBioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdf.jpgGenerated Thumbnailimage/jpeg5297https://repository.ucc.edu.co/bitstreams/822f0b0c-bcd6-4e51-bab5-560362f1a53b/download696b62d382a31029363dc37be54832b5MD56TEXTBiodata mining_Guauque._Repositorio.pdf.txtBiodata mining_Guauque._Repositorio.pdf.txtExtracted texttext/plain14061https://repository.ucc.edu.co/bitstreams/a3dea475-e30a-41fa-a35e-086fdb563305/download0ef7b4870a49f4e6c2740fa6a93eb80aMD57BioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdf.txtBioData Mining Licencia de uso RI Ver5 - 9-07-2021.doc.pdf.txtExtracted texttext/plain5887https://repository.ucc.edu.co/bitstreams/5da7638b-174a-46d8-9bdb-4bc0cf5baf54/download34afc0bded2490a3a6df3eb68016bcdaMD5820.500.12494/46729oai:repository.ucc.edu.co:20.500.12494/467292024-08-10 22:48:16.165open.accesshttps://repository.ucc.edu.coRepositorio Institucional Universidad Cooperativa de Colombiabdigital@metabiblioteca.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

Biological knowledge-slanted random forest approach for the classification of calcified aortic valve stenosis

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