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Borja Calvo
Researcher at University of the Basque Country
Publications - 41
Citations - 1826
Borja Calvo is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Bayesian probability & Context (language use). The author has an hindex of 15, co-authored 38 publications receiving 1546 citations.
Papers
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Journal ArticleDOI
Machine learning in bioinformatics
Pedro Larrañaga,Borja Calvo,Roberto Santana,Concha Bielza,Josu Galdiano,Iñaki Inza,Jose A. Lozano,Rubén Armañanzas,Guzmán Santafé,Aritz Pérez,Víctor Robles +10 more
TL;DR: Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.
Journal ArticleDOI
Differential micro RNA expression in PBMC from multiple sclerosis patients.
David Otaegui,Sergio E. Baranzini,Rubén Armañanzas,Borja Calvo,Maider Muñoz-Culla,Puya Khankhanian,Iñaki Inza,Jose A. Lozano,Tamara Castillo-Triviño,Ana Asensio,J. Olaskoaga,Adolfo López de Munain +11 more
TL;DR: Analysis of expression patterns of 364 miRNAs in PBMC obtained from multiple sclerosis patients in relapse status, in remission status and healthy controls reveals that two mi RNAs may be relevant at the time of relapse and that another miRNA may be involved in remission.
Journal ArticleDOI
scmamp: statistical comparison of multiple algorithms in multiple problems
Borja Calvo,Guzmán Santafé +1 more
TL;DR: Scmamp as discussed by the authors is an R package aimed at being a tool that simplifies the whole process of analyzing the results obtained when comparing algorithms, from loading the data to the production of plots and tables.
Book ChapterDOI
Machine learning: an indispensable tool in bioinformatics.
TL;DR: This chapter provides a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown.
Journal ArticleDOI
Learning Bayesian classifiers from positive and unlabeled examples
TL;DR: This work presents a new algorithm to obtain tree augmented naive Bayes models in the positive unlabeled domain, and proposes a new Bayesian approach to deal with the a priori probability of the positive class that models the uncertainty over this parameter by means of a Beta distribution.