title: Machine learning: an indispensable tool in bioinformatics.
abstract: The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.
authors: Intelligent Systems Group, Donostia - San Sebastián, Basque Country, Spain.
authors: Iñaki
authors: I
authors: Inza
authors: Borja
authors: B
authors: Calvo
authors: Rubén
authors: R
authors: Armañanzas
authors: Endika
authors: E
authors: Bengoetxea
authors: Pedro
authors: P
authors: Larrañaga
authors: José A
authors: JA
authors: Lozano
mesh_terms: D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000295:instrumentation* / Q000379:methods*; D016208:Databases, Factual; D001330:Electronic Data Processing; D012984:Software