Predicting Genes Involved in Human Cancer Using Network Contextual Information

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doi doi:10.2390/biecoll-jib-2012-210
submission April 29, 2012
last revision July 27, 2012
published September 05, 2012
NCBI PubMed PubMed ID 22948007

Hossein Rahmani, Hendrik Blockeel and Andreas Bender

Correspondence should be addressed to:
Hossein Rahmani
Leiden Institute of Advanced Computer Science, Universiteit Leiden, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands


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.


Hossein Rahmani, Hendrik Blockeel and Andreas Bender. Predicting Genes Involved in Human Cancer Using Network Contextual Information. Journal of Integrative Bioinformatics, 9(1):210, 2012. Online Journal:
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