2026-04-19T11:29:03Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3291902026-02-11T04:45:25Zcom_2072_1033col_2072_452950

Predicting Gastric Cancer Molecular Subtypes from Gene Expression Data Proceedings, 2020, XoveTIC 2020 Moreno, Marta Sousa, Abel Melé, Marta Oliveira, Rui Ferreira, Pedro G. Barcelona Supercomputing Center Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Cancer -- Molecular aspects Stomach--Cancer Gene expression Gene expression Gastric cancer Disease classification Machine learning Càncer -- Aspectes moleculars (This article belongs to the Proceedings of 3rd XoveTIC Conference) Stomach cancer is a complex disease and one of the leading causes of cancer mortality in the world. With the view to improve patient diagnosis and prognosis, it has been stratified into four molecular subtypes. In this work, we compare the results of multiple machine learning algorithms for the prediction of stomach cancer molecular subtypes from gene expression data. Moreover, we show the importance of decorrelating clinical and technical covariates. This work was supported by the FCT (Fundação para a Ciência e a Tecnologia) research grant Ph.D. Studentship SFRH/BD/145707/2019 and the research grant IF/01127/2014, funded in the scope of the FCT Investigator Exploratory Project: “Understanding the impact of acquired and germline genetic variants in the complexity of gastric cancer”; GenomePT project (reference 22184): “National Laboratory for Genome Sequencing and Analysis”; QREN L3 project (reference NORTE-01-0145-FEDER-000029): “Mapping genetic and phenotypic heterogeneity in HER2 positive cancers to anticipate and counteract resistance phenotypes”. Peer Reviewed Postprint (published version) 2020 Article Moreno, M. [et al.]. Predicting Gastric Cancer Molecular Subtypes from Gene Expression Data. "3rd XoveTIC Conference (Proceedings, 2020, vol. 54, núm. 1, 59)". 2504-3900 https://hdl.handle.net/2117/329190 10.3390/proceedings2020054059 eng https://www.mdpi.com/2504-3900/54/1/59 http://creativecommons.org/licenses/by/3.0/es/ https://creativecommons.org/licenses/by/4.0/ Open Access Attribution 3.0 Spain Attribution 4.0 International (CC BY 4.0) 4 p. application/pdf MDPI