An Unsupervised Multi-Omic Strategy to Investigate Shared and Tissue-Specific Resistance Mechanisms in Cancer

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UDC.coleccionInvestigación
UDC.conferenceTitleASEICA 2025
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.grupoInvLaboratorio de Aprendizaxe Automático en Ciencias Vivas (MALL)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
dc.contributor.authorGonzález-Carro, Sara
dc.contributor.authorMartín-Revuelta, Samuel
dc.contributor.authorFernández-Lozano, Carlos
dc.contributor.authorLiñares Blanco, José
dc.date.accessioned2026-09-01T10:26:32Z
dc.date.available2026-09-01T10:26:32Z
dc.date.issued2025
dc.descriptionPoster presentado en: 20th ASEICA International Congress, 8-10 October 2025, Bilbao. https://www.aseica.es/20th-aseica-international-congress
dc.description.abstract[Abstract]: This study presents MIRACS, an unsupervised multi-omic integration framework that analyzes transcriptomic and proteomic data to identify shared, cross-lineage molecular programs associated with cancer drug resistance.
dc.description.sponsorshipThis work was supported by European Programme Interreg VI-B Sudoe 2021–2027 (S1/1.1/P0033 - Drug Repurposing for Effective and Accelerated drug Development in the SUDOE Space) co-financed by ERDF funds (European Regional Development Fund), JLB is supported by the Galician Government through the fellowship ED481B_072
dc.description.sponsorshipXunta de Galicia; ED481B_072
dc.description.sponsorshipInterreg Sudoe; S1/1.1/P0033
dc.description.urihttps://www.aseica.es/20th-aseica-international-congress
dc.identifier.citationS. González-Carro, S. Martín Revuelta, C. Fernández-Lozano, and J. Liñares-Blanco, "An Unsupervised Multi-Omic Strategy to Investigate Shared and Tissue-Specific Resistance Mechanisms in Cancer" [poster], 20th ASEICA International Congress, 2025.
dc.identifier.urihttps://hdl.handle.net/2183/49124
dc.language.isoeng
dc.publisherAsociación Española de Investigación sobre el Cáncer (ASEICA)
dc.rights© 2025
dc.rights.accessRightsembargoed access
dc.subjectMulti-omics integration
dc.subjectCancer drug resistance
dc.subjectUnsupervised learning
dc.titleAn Unsupervised Multi-Omic Strategy to Investigate Shared and Tissue-Specific Resistance Mechanisms in Cancer
dc.typeconference output
dspace.entity.typePublication
relation.isAuthorOfPublicatione5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a
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relation.isAuthorOfPublication.latestForDiscoverye5ddd06a-3e7f-4bf4-9f37-5f1cf3d3430a

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