Predicting protein function via multi-label supervised topic model on gene ontology

As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions.