Computational Biology and Chemistry
Computational Biology and Chemistry
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Functional link artificial neural network-based disease gene prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A novel computational method for predicting disease genes based on functional similarity
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Leveraging health social networking communities in translational research
Journal of Biomedical Informatics
Detecting disease genes based on semi-supervised learning and protein-protein interaction networks
Artificial Intelligence in Medicine
Prioritizing disease genes by bi-random walk
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
International Journal of Knowledge Discovery in Bioinformatics
Seed-weighted random walk ranking for cancer biomarker prioritisation: a case study in leukaemia
International Journal of Data Mining and Bioinformatics
Protein Interactions for Functional Genomics
International Journal of Knowledge Discovery in Bioinformatics
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Motivation: Mining the hereditary disease-genes from human genome is one of the most important tasks in bioinformatics research. A variety of sequence features and functional similarities between known human hereditary disease-genes and those not known to be involved in disease have been systematically examined and efficient classifiers have been constructed based on the identified common patterns. The availability of human genome-wide protein--protein interactions (PPIs) provides us with new opportunity for discovering hereditary disease-genes by topological features in PPIs network. Results: This analysis reveals that the hereditary disease-genes ascertained from OMIM in the literature-curated (LC) PPIs network are characterized by a larger degree, tendency to interact with other disease-genes, more common neighbors and quick communication to each other whereas those properties could not be detected from the network identified from high-throughput yeast two-hybrid mapping approach (EXP) and predicted interactions (PDT) PPIs network. KNN classifier based on those features was created and on average gained overall prediction accuracy of 0.76 in cross-validation test. Then the classifier was applied to 5262 genes on human genome and predicted 178 novel disease-genes. Some of the predictions have been validated by biological experiments. Contact: jianzxu@hotmail.com Supplementary information: Supplementary data are available at Bioinformatics online.