PMC:4331678 / 34814-40240 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":"Network relevance estimation\nDifferent networks present different levels of quality for protein function prediction. To investigate whether MNet can assign a large weight to a network that can produce accurate predictions, and assign a small weight to a network that poorly predicts protein functions, we recorded the results of MNet (see Eq. (1)) for individual networks and the corresponding weights (αm). We also recorded the results and the weights of SW and ProMK on individual networks. For a fair comparison and a better visualization, we scale these weights in the interval [0, 1] as follows: αm/∑i=1Mαm.\nFigure 2 gives the Fmax values on the eight individual networks of the Human dataset (annotated with the BP labels), and the optimized weights on these networks. The corresponding results with respect to MacroF1 and fAUC are reported in Figures 3 and 4 of the Additional File 1. We also provide the results on the Human dataset (annotated with the CC and the MF labels) in the Additional File 1 (see Figures 5 and 6).\nFigure 2 Network relevance estimation using MNet, SW, and ProMK on the Human dataset annotated with BP functions. For each group of bars, the left one shows the Fmax value on the individual network, and the right one gives the weight assigned to the same network. From the Figure, we can observe that all three algorithms achieve the largest Fmax value on the 6-th network, and the Fmax value on each individual network has a similar rank among the eight individual networks across the different methods, i.e., the Fmax value on the 1st network ranks second according to MNet, SW, and ProMK. MNet assigns a larger weight on the 6-th network as compared to the weights for the other networks. In contrast, neither SW nor ProMK assigns the largest weight to the 6-th network. MNet, SW, and ProMK give the smallest weight to the 8-th network, though these methods do not produce the lowest Fmax for the 8-th network. The reason is that the 8-th network produces rather large smoothness loss values as compared to those of the other networks. Since λ2 is given a large value, ProMK assigns nearly equal weights to the first 7 networks. Because the smoothness loss value on the 8-th network is much larger than for the others, ProMK assigns zero weight to the 8-th network. Note that for small λ2 values, ProMK can only use one network and produces deteriorated results (see our parameter analysis in the next subsection). The Fmax values on the first three networks progressively decrease, and the weights assigned by MNet and SW to these networks also decrease. In contrast, the weights assigned by ProMK do not follow this trend. ProMK assigns larger weights to the 2nd and 3rd networks. The Fmax values on the next three (4-th, 5-th, and 6-th) networks, as well as the weights assigned by MNet, progressively increase, but the weight assigned by SW to the 4-th network is larger than those assigned to the 5-th and 6-th networks, and the weights assigned by ProMK progressively decrease. All these three methods give a smaller Fmax value to the 7-th network than to the 6-th; both MNet and SW assign a smaller weight to the 7-th network than to the 6-th, but ProMK assigns a larger weight to the 7-th network than to the 6-th. ProMK, OMG and LIG use only the smoothness loss to assign weights to the individual networks. The smaller the value of the smoothness loss for a network is, the larger the weight assigned to it is. The value of the smoothness loss of ProMK on the 3rd network is smaller than the values of the other networks, thus ProMK assigns a weight to the 3rd network that is larger than the ones assigned to other networks. However, the value of Fmax of this network is the lowest. This conflictual scenario shows that assigning a weight to a network merely based on the smoothness loss is not always reasonable. This justifies our motivation to unifying the kernel target alignment with the loss of classifier in one objective function, and also provides evidence as for why MNet works better than the other algorithms. These observations also apply to the results provided in the Additional File 1.\nAnother interesting observation for Figure 6 in the Additional File 1 is that MNet, SW, and ProMK give the highest Fmax value to the 1st network of the Human dataset (annotated with MF functions), instead of to the 6-th network. In the Human dataset, the 1st network is derived from protein domain composition and the 6-th is a PPI network. This observation supports the statement that different data sources have different correlation with the GO terms. Lan et al. [7] also observed that the prediction of MF functions using sequence similarity is more accurate than that based on PPI information, and the prediction of BP functions based on PPI networks is more reliable than that based on sequence similarity. Regardless of this difference for the proteins of Human annotated with MF functions, MNet shows similar trends for the weight and the Fmax values assigned to the individual networks. In contrast, neither SW nor ProMK manifests such behavior.\nIf we take the Fmax value of an individual network as its quality, we can conclude that MNet can assign weights to the individual networks that are proportional to their quality, whereas SW and ProMK cannot. This observation also helps us understand why MNet achieves a performance that is better than that of SW and ProMK.","divisions":[{"label":"title","span":{"begin":0,"end":28}},{"label":"p","span":{"begin":29,"end":612}},{"label":"p","span":{"begin":613,"end":1030}},{"label":"figure","span":{"begin":1031,"end":1295}},{"label":"label","span":{"begin":1031,"end":1039}},{"label":"caption","span":{"begin":1041,"end":1295}},{"label":"p","span":{"begin":1041,"end":1295}},{"label":"p","span":{"begin":1296,"end":4147}},{"label":"p","span":{"begin":4148,"end":5102}}],"tracks":[{"project":"2_test","denotations":[{"id":"25707434-23514608-14839093","span":{"begin":4615,"end":4616},"obj":"23514608"}],"attributes":[{"subj":"25707434-23514608-14839093","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#ec939c","default":true}]}]}}