PMC:4331678 / 2772-3585 JSONTXT

{"project":"2_test","denotations":[{"id":"25707434-15130933-14839053","span":{"begin":735,"end":736},"obj":"15130933"},{"id":"25707434-20507895-14839054","span":{"begin":737,"end":739},"obj":"20507895"}],"text":"A number of computational methods have been suggested to integrate heterogeneous data for inferring protein (or gene) functions [6,13]. Most of these techniques follow the same basic paradigm: first, they generate various functional association networks (one or more networks for one data source) that encode the implicit information of shared functions of proteins in each data source. Then these individual networks (or kernels) are combined, through a weighted sum, into a composite network, where the weights are optimized using labels, each label corresponding to a distinct protein function. Next, the composite network, along with the function labels, are given in input to a network (or kernel) based classification algorithm [5,14-16] to compute the likelihood of a specific function label for a protein."}