Title: Predicting genes involved in human cancer using network contextual information
Authors: Rahmani, Hossein ×
Blockeel, Hendrik
Bender, Andreas #
Issue Date: 2012
Publisher: IMBio e.V. c/o R. Hofestädt c/o Bielefeld University, Faculty of Technology, Bioinformatics Department.
Series Title: Journal of Integrative Bioinformatics vol:9 issue:1 pages:1-28
Article number: 210
Abstract: Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance) based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes) that uses the features proposed here outperforms the current state-of-the-art methods by
more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.
ISSN: 1613-4516
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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