One of the main problems in functional genomics is the prediction of the unknown gene/protein functions. With the rapid increase of high-throughput technologies, the vast amount of biological data describing different aspects of cellular functioning became available and made it possible to use them as the additional information sources for function prediction and to improve their accuracy.In our research, we have described an approach to protein function prediction on the basis of integration of several biological datasets. Initially, each dataset is presented in the form of a graph (or network), where the nodes represent genes or their products and the edges represent physical, functional or chemical relationships between nodes. The integration process makes it possible to estimate the network importance for the prediction of a particular function taking into account the imbalance between the functional annotations, notably the disproportion between positively and negatively annotated proteins. The protein function prediction consists in applying the label propagation algorithm to the integrated biological network in order to annotate the unknown proteins or determine the new function to already known proteins. The comparative analysis of the prediction efficiency with several integration schemes shows the positive effect in terms of several performance measures.
An efficient strategy for extensive integration of diverse biological data for protein function prediction
