Zobrazeno 1 - 10
of 23
pro vyhledávání: '"André Veríssimo"'
Autor:
Marta B. Lopes, André Veríssimo, Eunice Carrasquinha, Sandra Casimiro, Niko Beerenwinkel, Susana Vinga
Publikováno v:
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-15 (2018)
Abstract Background Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed invers
Externí odkaz:
https://doaj.org/article/2e73e1be010c4d1f9ef4b704c68dcddb
Publikováno v:
BioData Mining, Vol 11, Iss 1, Pp 1-14 (2018)
Abstract Background Survival analysis is a statistical technique widely used in many fields of science, in particular in the medical area, and which studies the time until an event of interest occurs. Outlier detection in this context has gained grea
Externí odkaz:
https://doaj.org/article/23733c4e2afb42ba8207f70ed23e6a5c
Autor:
Laura Paixão, Joana Oliveira, André Veríssimo, Susana Vinga, Eva C Lourenço, M Rita Ventura, Morten Kjos, Jan-Willem Veening, Vitor E Fernandes, Peter W Andrew, Hasan Yesilkaya, Ana Rute Neves
Publikováno v:
PLoS ONE, Vol 10, Iss 3, p e0121042 (2015)
The human pathogen Streptococcus pneumoniae is a strictly fermentative organism that relies on glycolytic metabolism to obtain energy. In the human nasopharynx S. pneumoniae encounters glycoconjugates composed of a variety of monosaccharides, which c
Externí odkaz:
https://doaj.org/article/b7ebd0e9277948dabb5ee5e0c3fb79ab
Autor:
André Veríssimo
Publikováno v:
Experiências de Vanguarda no ensino nos países Lusófonos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0fca3924953049982dbcd44a9d89a401
https://doi.org/10.23899/9786589284093.25
https://doi.org/10.23899/9786589284093.25
Publikováno v:
Computational Intelligence Methods for Bioinformatics and Biostatistics ISBN: 9783030630607
CIBB
CIBB
Random sample consensus (Ransac) is a technique that has been widely used for modeling data with a large amount of noise. Although successfully employed in areas such as computer vision, extensive testing and applications to clinical data, particular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12c0193c9245f9b3fa3dc62c467938fb
https://doi.org/10.1007/978-3-030-63061-4_11
https://doi.org/10.1007/978-3-030-63061-4_11
Publikováno v:
Bioinformatics and Biomedical Engineering ISBN: 9783030453848
IWBBIO
IWBBIO
The accessibility to “big data” sets down an ambitious challenge in the medical field, especially in personalized medicine, where gene expression data are increasingly being used to establish a diagnosis and optimize treatment of oncological pati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c768eaf1b6f0f172a27516e327faa68
https://doi.org/10.1007/978-3-030-45385-5_49
https://doi.org/10.1007/978-3-030-45385-5_49
Autor:
Sandra Casimiro, Marta B. Lopes, Niko Beerenwinkel, Susana Vinga, Eunice Carrasquinha, André Veríssimo
Publikováno v:
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-15 (2018)
BMC Bioinformatics, 19
BMC Bioinformatics
BMC Bioinformatics, 19
BMC Bioinformatics
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
LOD
The importance of gene expression data analysis for oncological diagnosis and treatment has become widely accepted in recent years. One of the main associated challenges is the development of mathematical and statistical methods for data analysis to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3073981b3f9fd4a10b027eaf54f3ca3b
https://doi.org/10.1007/978-3-030-13709-0_36
https://doi.org/10.1007/978-3-030-13709-0_36
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030375980
LOD
LOD
Network information is gaining importance in the generation of predictive models in cancer genomics, with the premise that prior biological knowledge offers the models interpretability and reproducibility, an invaluable contribution in precision medi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fa45660fff40a84c1a3478272b86e44
https://doi.org/10.1007/978-3-030-37599-7_52
https://doi.org/10.1007/978-3-030-37599-7_52
Autor:
Susana Vinga, Marta B. Lopes, Eunice Carrasquinha, Arlindo L. Oliveira, André Veríssimo, Marie-France Sagot
Data availability by modern sequencing technologies represents a major challenge in oncological survival analysis, as the increasing amount of molecular data hampers the generation of models that are both accurate and interpretable. To tackle this pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c832a0778918315366510e35aa88a53