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Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity
dc.contributor.author | Torres Martos, Álvaro | |
dc.contributor.author | Bustos Aibar, Mireia | |
dc.contributor.author | Ramírez Mena, Alberto | |
dc.contributor.author | Cámara Sánchez, Sofía | |
dc.contributor.author | Anguita Ruiz, Augusto | |
dc.contributor.author | Alcalá Fernández, Rafael | |
dc.contributor.author | Aguilera García, Concepción María | |
dc.contributor.author | Alcalá Fernández, Jesús | |
dc.date.accessioned | 2023-03-28T06:40:24Z | |
dc.date.available | 2023-03-28T06:40:24Z | |
dc.date.issued | 2023-01-18 | |
dc.identifier.citation | Torres-Martos, Á... [et al.]. Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity. Genes 2023, 14, 248. [https://doi.org/10.3390/genes14020248] | es_ES |
dc.identifier.uri | https://hdl.handle.net/10481/80881 | |
dc.description.abstract | The use of machine learning techniques for the construction of predictive models of disease outcomes (based on omics and other types of molecular data) has gained enormous relevance in the last few years in the biomedical field. Nonetheless, the virtuosity of omics studies and machine learning tools are subject to the proper application of algorithms as well as the appropriate preprocessing and management of input omics and molecular data. Currently, many of the available approaches that use machine learning on omics data for predictive purposes make mistakes in several of the following key steps: experimental design, feature selection, data pre-processing, and algorithm selection. For this reason, we propose the current work as a guideline on how to confront the main challenges inherent to multi-omics human data. As such, a series of best practices and recommendations are also presented for each of the steps defined. In particular, the main particularities of each omics data layer, the most suitable preprocessing approaches for each source, and a compilation of best practices and tips for the study of disease development prediction using machine learning are described. Using examples of real data, we show how to address the key problems mentioned in multi-omics research (e.g., biological heterogeneity, technical noise, high dimensionality, presence of missing values, and class imbalance). Finally, we define the proposals for model improvement based on the results found, which serve as the bases for future work. | es_ES |
dc.description.sponsorship | ERDF/Regional Government of Andalusia/Ministry of Economic Transformation, Industry, Knowledge, and Universities P18-RT-2248 B-CTS-536-UGR20 | es_ES |
dc.description.sponsorship | ERDF/Health Institute Carlos III/Spanish Ministry of Science, Innovation PI20/00711 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Machine learning | es_ES |
dc.subject | Omics | es_ES |
dc.subject | Data pre-processing | es_ES |
dc.title | Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.3390/genes14020248 | |
dc.type.hasVersion | VoR | es_ES |