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Open Access Highly Accessed Methodology article

Incorporating functional inter-relationships into protein function prediction algorithms

Gaurav Pandey*, Chad L Myers and Vipin Kumar

Author Affiliations

Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA

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BMC Bioinformatics 2009, 10:142  doi:10.1186/1471-2105-10-142

Published: 12 May 2009

Additional files

Additional file 1:

Details of the GO classes used for evaluation. Details of the 138 functional classes from the GO Biological Process ontology whose subsets (classes having at least 10 members in the corresponding data set) are used for evaluation using several genomic data sets in this study.

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Additional file 2:

Arrangement of the functional classes aiding the improvement of the AUC score of the GO:0051049 (regulation of transport) class in the GO biological process ontology. This figure shows the arrangement of the functional classes aiding the improvement of the AUC score of the GO:0051049 (regulation of transport) class (listed in Table 4) in the GO biological process ontology. Their structural proximity to the target class (GO:0051049) suggests their potential to help improve the predictions for this class.

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Additional file 3:

Comparison of AUC scores from our approach and GEST. This figure shows the comparison of the performance of our functional similarity-incorporated k-NN classifiers with individual GEST classifiers for Mnaimneh et al's data set.

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Additional file 4:

Ranked list of predictions from the Mnaimneh gene expression data set. A detailed list of ranked predictions produced by the label similarity-incorporated kNN classifiers (first worksheet) and base kNN classifiers (second worksheet) for the test genes extracted from the Mnaimneh gene expression data set. The GO terms, arranged in columns, are sorted from left to right in the order of decreasing AUC improvements by incorporating functional relationships into their base classifiers. The genes in each column are ranked in descending order by the score assigned by the corresponding kNN classifier. Genes with the same score (mostly in the case when the score is 0) are sorted by their ORF name.

Format: XLS Size: 6.1MB Download file

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Additional file 5:

Ranked list of predictions from the Rosetta gene expression data set. A detailed list of ranked predictions produced by the label similarity-incorporated kNN classifiers (first worksheet) and base kNN classifiers (second worksheet) for the test genes extracted from the Rosetta gene expression data set. The GO terms, arranged in columns, are sorted from left to right in the order of decreasing AUC improvements by incorporating functional relationships into their base classifiers. The genes in each column are ranked in descending order by the score assigned by the corresponding kNN classifier. Genes with the same score (mostly in the case when the score is 0) are sorted by their ORF name.

Format: XLS Size: 6MB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data