dc.creator | Linares Barrera, María Lourdes | es |
dc.creator | Martínez Ballesteros, María del Mar | es |
dc.creator | García Heredia, José Manuel | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2024-04-10T10:00:39Z | |
dc.date.available | 2024-04-10T10:00:39Z | |
dc.date.issued | 2023-08 | |
dc.identifier.citation | Linares Barrera, M.L., Martínez Ballesteros, M.d.M., García Heredia, J.M. y Riquelme Santos, J.C. (2023). A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma. En Hybrid Artificial Intelligent Systems (HAIS 2023) (731-742), Salamanca (España): SpringerLink. | |
dc.identifier.isbn | 978-3-031-40724-6 | es |
dc.identifier.isbn | 978-3-031-40725-3 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/156743 | |
dc.description.abstract | Sarcomas are rare mesodermal tumors of heterogeneous nature and have a higher incidence in children. The relative 5-year survival rate for patients with metastatic sarcoma is usually low. Standard treatment for sarcomas involves surgical resection, and investigating the
genetic basis of these tumors through genome-wide analysis is crucial due to their rarity and late diagnosis. This work proposes a methodology that combines preprocessing, feature selection and association rule mining to identify relevant genes and significant relationships in biological data from sarcoma patients. Our study aims to identify the relationships between metastasis-associated genes and patient survival of less than 5 years. The proposed approach was applied to a sarcoma dataset containing data on gene expression, metastasis occurrence, and survival time, revealing a set of biologically relevant gene interactions associated with sarcoma metastasis and low survival rates. The combined use of these techniques can facilitate the identification of biomarkers or gene signatures associated with the disease and provide insight into the underlying biological mechanisms involved in sarcomas. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-117954RB-C22 | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TED2021-131311B-C21 | es |
dc.description.sponsorship | Junta de Andalucía PYC20 RE 078 USE | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | SpringerLink | es |
dc.relation.ispartof | Hybrid Artificial Intelligent Systems (HAIS 2023) (2023), pp. 731-742. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Feature Selection | es |
dc.subject | Association Rules | es |
dc.subject | Gene Expression | es |
dc.subject | Sarcoma | es |
dc.title | A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2020-117954RB-C22 | es |
dc.relation.projectID | TED2021-131311B-C21 | es |
dc.relation.projectID | PYC20 RE 078 USE | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-031-40725-3_62 | es |
dc.identifier.doi | 10.1007/978-3-031-40725-3_62 | es |
dc.publication.initialPage | 731 | es |
dc.publication.endPage | 742 | es |
dc.eventtitle | Hybrid Artificial Intelligent Systems (HAIS 2023) | es |
dc.eventinstitution | Salamanca (España) | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |