The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
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Many entries in major biological databases have incomplete functional annotation and thus, frequently, it is difficult to identify entries for a specific functional category. We combined information of protein functional domains and gene ontology descriptions for highly accurate identification of transcription factor (TF) entries in Swiss-Prot and Entrez Gene databases. Our method utilizes support vector machines and it efficiently separates TF entries from non-TF entries. The 10-fold cross validation of predictions produced on average a positive predictive value of 97.5% and sensitivity of 93.4%. Using this method we have scanned the whole Swiss-Prot and Entrez Gene databases and extracted 13826 unique TF entries. Based on a separate manual test of 500 randomly chosen extracted TF entries, we found that the non-TF (erroneous) entries were present in 2% of the cases.