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BackgroundComparing and classifying functions of gene products are important in today’s biomedical research. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most widely used indicators for protein interaction. Among the various approaches proposed, those based on the vector space model are relatively simple, but their effectiveness is far from satisfying.ResultsWe propose a Hierarchical Vector Space Model (HVSM) for computing semantic similarity between different genes or their products, which enhances the basic vector space model by introducing the relation between GO terms. Besides the directly annotated terms, HVSM also takes their ancestors and descendants related by “is_a” and “part_of” relations into account. Moreover, HVSM introduces the concept of a Certainty Factor to calibrate the semantic similarity based on the number of terms annotated to genes. To assess the performance of our method, we applied HVSM to Homo sapiens and Saccharomyces cerevisiae protein-protein interaction datasets. Compared with TCSS, Resnik, and other classic similarity measures, HVSM achieved significant improvement for distinguishing positive from negative protein interactions. We also tested its correlation with sequence, EC, and Pfam similarity using online tool CESSM.ConclusionsHVSM showed an improvement of up to 4% compared to TCSS, 8% compared to IntelliGO, 12% compared to basic VSM, 6% compared to Resnik, 8% compared to Lin, 11% compared to Jiang, 8% compared to Schlicker, and 11% compared to SimGIC using AUC scores. CESSM test showed HVSM was comparable to SimGIC, and superior to all other similarity measures in CESSM as well as TCSS. Supplementary information and the software are available at https://github.com/kejia1215/HVSM.

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The K-Medoids clustering algorithm solves the problem of the K-Means algorithm on processing the outlier samples, but it is not be able to process big-data because of the time complexity[1]. MapReduce is a parallel programming model for processing big-data, and has been implemented in Hadoop. In order to break the big-data limits, the parallel KMedoids algorithm HK-Medoids based on Hadoop was proposed. Every submitted job has many iterative MapReduce procedures: In the map phase, each sample was assigned to one cluster whose center is the most similar with the sample; in the combine phase, an intermediate center for each cluster was calculated; and in the reduce phase, the new center was calculated. The iterator stops when the new center is similar to the old one. The experimental results showed that HK-Medoids algorithm has a good clustering result and linear speedup for big-data.