PMC:4331678 / 8610-9915 JSONTXT

{"project":"2_test","denotations":[{"id":"25707434-20507895-14839069","span":{"begin":222,"end":224},"obj":"20507895"}],"text":"More recently, some researchers advocated for the computation of optimal weights on individual networks for a group of labels, and achieved better performance than the methods operating on single labels. Mostafavi et al. [16] introduced a method, called SW, that simultaneously optimizes the weights on individual networks with respect to a group of related functional labels by solving a single-constrained linear regression problem. The optimal weights maximize a form of kernel-target alignment [24] between the composite network and the target network, which is defined based on the functional relationships implied by the functions of proteins. However, merely maximizing the kernel target alignment does not necessarily result in an optimal composite network for the network-based classifier. Yu et al. [25] proposed a method, called ProMK, that combines the composite network optimization with respect to a group of functions and the network based classifier in a unified objective function. ProMK can selectively integrate multiple networks and can construct an optimal composite network directly targeted to the network based classification, but it suffers from the parameter selection problem, and does not take into account the intrinsic unbalanced label problem in protein function prediction."}