[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121452-en":3,"doc-seo-121452-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121452,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Discovering Genetic Variants in Hypertrophic Cardiomyopathy With Multiple Machine Learning Techniques","Hypertrophic cardiomyopathy is shaped by genetic causes, yet only part of the literature captures how co-expressed genes and variants jointly influence phenotype. This study performs relevance and interaction analysis of genetic variants from hypertrophic cardiomyopathy patients using multiple machine learning pipelines: statistical univariate testing, linear classifiers for combined feature weights, Bayesian and variable-identifier modeling, manifold learning for latent embeddings, and linkage disequilibrium plus frequency analyses. Results use 61 patients and 67 controls covering 216 variants from a 15-gene panel.","Discovering Genetic Variants in Hypertrophic Cardiomyopathy With Multiple Machine Learning Techniques  \nDafne Lozano-Paredes , Luis Bote-Curiel , María Sabater-Molina, Concha Bielza , Senior Member, IEEE, Juan R Gimeno-Blanes, Sergio Muñoz-Romero , F Javier Gimeno-Blanes , Pedro Larrañaga , Fellow, IEEE,  \nand José Luis Rojo-Álvarez , Senior Member, IEEE  \nAbstract—Hypertrophic cardiomyopathy is known to have strong genetic foundations. However, only some studies have addressed the complex network of co-expressed genes and variants that modify the phenotype. Machine learning methods offer robust information discovery when dealing with high-dimensional datasets. We aimed to perform relevance and interaction analysis on genetic variants from hypertrophic cardiomyopathy patients using diverse machine learning techniques, with the following stages:(a) Statistical univariate techniques (with various p-value adjustment methods) identified relevant variants; (b) Linear classifiers (support vector machines, Fisher discriminant analysis) provided combined relevance based on feature weights; (c) Informative  \nReceived 21 November 2024; revised 5 April 2025; accepted 19 May 2025 . Date of publication 26 May 2025; date of current version 8 August 2025 . This work was supported in part by the Ministry of Science and Innovation / State Research Agency (MCIN/AEI/10.13039/501100011033), in part by PI22/01042, in part by SEC/FEC-INV-BAS 21/021 under Project PID2022-140786NBC31 (LATENTIA), Project PID2022-140553OA-C42 (PCardioTrials), Project PID2022-139977NB-I00 (EDAS-ML-OPT), and Project TED2021-131310BI00, in part by the Spanish Society of Cardiology and the Spanish Heart Foundation, and in part by the ELLIS Unit Madrid from the Autonomous Region of Madrid. (Corresponding author: Dafne Lozano-Paredes.)  \nThis work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Ethics Committee ofthe Hospital Universitario Virgen dela Arrixaca under Application No. 2022-2-17-HCUVA, and performed in line with the Declaration of Helsinki. Written informed consent was obtained from all patients prior to inclusion in the study.  \nDafne Lozano-Paredes, Luis Bote-Curiel, Sergio Muñoz-Romero, and José Luis Rojo-Álvarez are with the Department of Signal Theory and Communications, Telematics, and Computing Systems, Universidad Rey Juan Carlos, 28942 Madrid, Spain (e-mail: [dafne.lozanop@urjc.es](dafne.lozanop@urjc.es)).  \nMaría Sabater-Molina is with the Cardiogenetics Laboratory, Instituto Murciano de Investigación Biosanitaria, 30120 Murcia, Spain, also with the Department of Legal and Forensic Medicine, Universidad de Murcia, 30003 Murcia, Spain, also with the CIBER Cardiovascular, Instituto de Salud Carlos III, 28029 Madrid, Spain, and also with the European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart), 1105, AZ Amsterdam, The Netherlands.  \nConcha Bielza and Pedro Larrañaga are with the Department of Artificial Intelligence, Universidad Politécnica de Madrid, 28660 Madrid, Spain.  \nJuan R Gimeno-Blanes is with the Familiar Cardiopathies Unit, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain, also with the Department of Internal Medicine, Universidad de Murcia, 30003 Murcia, Spain, also with the European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart), 1105, AZ Amsterdam, The Netherlands, and also with the CIBER Cardiovascular, Instituto de Salud Carlos III, 28029 Madrid, Spain.  \nF Javier Gimeno-Blanes is with the Department of Signal Theory and Communications, Universidad Miguel Hernández, 03202 Alicante, Spain.  \nDigital Object Identifier 10.1109/TCBBIO.2025.3572833  \nvariable identifier method and Bayesian networks explained intervariant relationships; (d) Manifold learning of low-dimensional latent spaces gave interpretable representations ofgroups;(e)Linkag","cbCaiqtwm5CH9Joy","https://ap.wps.com/l/cbCaiqtwm5CH9Joy","pdf",4330244,1,14,"English","en",105,"# 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