PMC:4331678 / 43117-45068 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4331678","sourcedb":"PMC","sourceid":"4331678","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4331678","text":"Runtime analysis\nWe also recorded the running times of MNet and the other comparing methods on the Yeast and Human datasets. The results are given in Table 2. All the methods are implemented in Matlab (R2011a 64-bit). The specification of the experiment platform is: CentOS 5.6, Intel Xeon X5650 and 32GB RAM.\nFrom Table 2, we can observe that MNet often takes more time than the other methods. As the number of functional labels reduces, the runtime cost of MNet decreases sharply. The reason is that MNet has to compute the trace norm not only for individual networks, but also for the pairwise networks (see Eq. (7)). In contrast, ProMK, OMG, and LIG only compute the trace norm for individual networks. The running time of MNet is often no more than M (the number of individual networks) times the cost of ProMK, which is consistent with our previous complexity analysis. MSkNN does not learn weights on individual networks; as such it always runs faster than the other methods. SW first applies kernel target alignment to fuse multiple networks into a composite one, and then predicts protein functions using the composite network; it often ranks second (from fastest to lowest) among the comparing methods. Both ProMK and OMG iteratively optimize the weights on individual networks; they have similar runtime costs and lose to SW and MSkNN. LIG takes more time than the other methods; sometimes it is also slower than MNet. The reason is that LIG applies time-consuming eigen-decomposition for soft spectral clustering to divide each individual network into several subnetworks, and then combines these subnetworks into a composite one for function prediction. Given the superior effectiveness of MNet, it is desirable to use MNet to integrate multiple networks for protein function prediction. However, seeking efficient and effective ways to utilize multiple networks for function prediction remains an important research direction to explore.","divisions":[{"label":"title","span":{"begin":0,"end":16}},{"label":"p","span":{"begin":17,"end":309}}],"tracks":[]}