title: Discriminative and informative features for biomolecular text mining with ensemble feature selection.
abstract: BACKGROUND
  In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results.
  
  
  RESULTS
  We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools.
  
  
  BACKGROUND
  The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).
authors.affiliation: Department of Plant Systems Biology, VIB, Ghent University, Gent, Belgium.
authors.forename: Sofie
authors.initials: S
authors.lastname: Van Landeghem
authors.forename: Thomas
authors.initials: T
authors.lastname: Abeel
authors.forename: Yvan
authors.initials: Y
authors.lastname: Saeys
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D015870:Gene Expression; D008660:Metabolism; D010766:Phosphorylation; D011485:Protein Binding; D014158:Transcription, Genetic
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