dc.contributor | Vall d'Hebron Barcelona Hospital Campus |
dc.contributor.author | Liñares Blanco, Jose |
dc.contributor.author | Fernandez-Lozano, Carlos |
dc.contributor.author | Seoane Fernández, Jose Antonio |
dc.contributor.author | Lopez-Campos, Guillermo |
dc.date.accessioned | 2022-09-09T07:40:40Z |
dc.date.available | 2022-09-09T07:40:40Z |
dc.date.issued | 2022-05-17 |
dc.identifier.citation | Liñares-Blanco J, Fernandez-Lozano C, Seoane JA, López-Campos G. Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes. Front Microbiol. 2022 May 17;13:872671. |
dc.identifier.issn | 1664-302X |
dc.identifier.uri | https://hdl.handle.net/11351/8090 |
dc.description | Malaltia de Crohn; Microbioma; Colitis ulcerosa |
dc.description.sponsorship | CF-L's work was supported by the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER)–A way to build Europe. JS's work was funded by the Ramón y Cajal grant (RYC2019-026576-I) funded by Ministry of Science and Innovation of the Spanish government. GL-C's work was supported by a grant from the Biotechnology and Biological Sciences Research Council (BBSRC grant BB/S006281/1) and open access publication fees were supported by Queen's University of Belfast UKRI block grant. |
dc.language.iso | eng |
dc.publisher | Frontiers Media |
dc.relation.ispartofseries | Frontiers in Microbiology;13 |
dc.rights | Attribution 4.0 International |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ |
dc.source | Scientia |
dc.subject | Aprenentatge automàtic |
dc.subject | Intestins - Inflamació - Diagnòstic |
dc.subject | Intestins - Microbiologia |
dc.subject.mesh | Mycobiome |
dc.subject.mesh | Inflammatory Bowel Diseases |
dc.subject.mesh | /diagnosis |
dc.subject.mesh | Machine Learning |
dc.title | Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes |
dc.type | info:eu-repo/semantics/article |
dc.identifier.doi | 10.3389/fmicb.2022.872671 |
dc.subject.decs | micobioma |
dc.subject.decs | enfermedad inflamatoria intestinal |
dc.subject.decs | /diagnóstico |
dc.subject.decs | aprendizaje automático |
dc.relation.publishversion | https://doi.org/10.3389/fmicb.2022.872671 |
dc.type.version | info:eu-repo/semantics/publishedVersion |
dc.audience | Professionals |
dc.contributor.organismes | Institut Català de la Salut |
dc.contributor.authoraffiliation | [Liñares-Blanco J] Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC, University of A Coruña, A Coruña, Spain. GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government PTS Granada, Granada, Spain. Department of Statistics and Operational Research, University of Granada, Granada, Spain. [Fernandez-Lozano C] Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC, University of A Coruña, A Coruña, Spain. [Seoane JA] Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain. [López-Campos G] Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, United Kingdom |
dc.identifier.pmid | 35663898 |
dc.identifier.wos | 000806106000001 |
dc.relation.projectid | info:eu-repo/grantAgreement/ES/PE2017-2020/RYC2019-026576-I |
dc.rights.accessrights | info:eu-repo/semantics/openAccess |