Observatorio de I+D+i UPM

Memorias de investigación
Consensus Policies to Solve Bioinformatic Problems
Research Areas
  • Artificial intelligence
Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. One of the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian networks are of the most useful. In optimization, classical search strategies are unfeasible to deal with high-dimensionality biological problems, where the current computer power is still insufficient to provide exhaustive searches. Therefore, machine learning and optimization procedures need accommodation to the specificities of the novel biological data. This book aims to contribute to the state-of-the-art of machine learning techniques adapted for dealing with computational biology problems.
Book Edition
Book Pulbishing
LAP LAMBERT Academic Publishing
  • Autor: Ruben Armañanzas Arnedillo (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)