dc.contributor
Barcelona Supercomputing Center
dc.contributor.author
Moreno, Marta
dc.contributor.author
Sousa, Abel
dc.contributor.author
Melé, Marta
dc.contributor.author
Oliveira, Rui
dc.contributor.author
Ferreira, Pedro G.
dc.identifier
Moreno, M. [et al.]. Predicting Gastric Cancer Molecular Subtypes from Gene Expression Data. "3rd XoveTIC Conference (Proceedings, 2020, vol. 54, núm. 1, 59)".
dc.identifier
https://hdl.handle.net/2117/329190
dc.identifier
10.3390/proceedings2020054059
dc.description.abstract
(This article belongs to the Proceedings of 3rd XoveTIC Conference)
dc.description.abstract
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.
dc.description.abstract
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”.
dc.description.abstract
Peer Reviewed
dc.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.relation
https://www.mdpi.com/2504-3900/54/1/59
dc.rights
http://creativecommons.org/licenses/by/3.0/es/
dc.rights
https://creativecommons.org/licenses/by/4.0/
dc.rights
Attribution 3.0 Spain
dc.rights
Attribution 4.0 International (CC BY 4.0)
dc.subject
Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subject
Cancer -- Molecular aspects
dc.subject
Stomach--Cancer
dc.subject
Gene expression
dc.subject
Gene expression
dc.subject
Gastric cancer
dc.subject
Disease classification
dc.subject
Machine learning
dc.subject
Càncer -- Aspectes moleculars
dc.title
Predicting Gastric Cancer Molecular Subtypes from Gene Expression Data
dc.title
Proceedings, 2020, XoveTIC 2020
