Comparison with other methods Recently, Tjong and Zhou (2007) developed a neural network method for predicting DNA-binding sites. In their method, for each surface residue, the PSSM and solvent accessibilities of the residue and its 14 neighbors were used as input to a neural network in the form of vectors. In their publication, Tjong and Zhou showed that their method achieved better performance than other previously published methods. In the current study, the 13 test proteins were obtained from the study of Tjong and Zhou. Thus, we can compare the method proposed in the current study with Tjong and Zhou’s neural network method using the 13 proteins. Figure 1. Tradeoff between coverage and accuracy In their publication, Tjong and Zhou also used coverage and accuracy to evaluate the predictions. However, they defined accuracy using a loosened criterion of “true positive” such that if a predicted interface residue is within four nearest neighbors of an actual interface residue, then it is counted as a true positive. Here, in the comparison of the two methods, the strict definition of true positive is used, i.e., a predicted interface residue is counted as true positive only when it is a true interface residue. The original data were obtained from table 1 of Tjong and Zhou (2007), the accuracy for the neural network method was recalculated using this strict definition (Table 3). The coverage of the neural network was directly taken from Tjong and Zhou (2007). For each protein, Tjong and Zhou’s method reported one coverage and one accuracy. In contrast, the method proposed this study allows the users to tradeoff between coverage and accuracy based on their actual need. For the purpose of comparison, for each test protein, topranking patches are included into the set of predicted interface residues one by one in the decreasing order of ranks until coverage is the same as or higher than the coverage that the neural network method achieved on that protein. Then the coverage and accuracy of the two methods are compared. On a test protein, method A is better than B, if accuracy(A)>accuracy(B) and coverage (A)≥coverage(B). Table 3 shows that the graph kernel method proposed in this study achieves better results than the neural network method on 7 proteins (in bold font in table 3). On 4 proteins (shown in gray shading in table 3), the neural network method is better than the graph kernel method. On the remaining 2 proteins (in italic font in table 3), conclusions can be drawn because the two conditions, accuracy(A)>accuracy(B) and coverage (A)≥coverage(B), never become true at the same time, i.e., when coverage (graph kernel)>coverage(neural network), we have accuracy(graph kernel)accuracy(neural network). Note that the coverage of the graph kernel method increases in a discontinuous fashion as we use more patches to predict DNA-binding sites. One these two proteins, we were not able to reach at a point where the two methods have identical coverage. Given these situations, we consider that the two methods tie on these 2 proteins. Thus, these comparisons show that the graph kernel method can achieves better results than the neural network on 7 of the 13 proteins (shown in bold font in Table 3). Additionally, on another 4 proteins (shown in Italic font in Table 3), the graph kernel method ties with the neural network method. When averaged over the 13 proteins, the coverage and accuracy for the graph kernel method are 59% and 64%. It is worth to point out that, in the current study, the predictions are made using the protein structures that are unbound with DNA. In contrast, the data we obtained from Tjong and Zhou’s study were obtained using proteins structures bound with DNA. In their study, Tjong and Zhou showed that when unbound structures were used, the average coverage decreased by 6.3% and average accuracy by 4.7% for the 14 proteins (but the data for each protein was not shown).
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Common Commands for Switches and Routers Switch> Switch> enable Switch# Switch# configure terminal Switch(config)# exit Switch# config t user mode privilege mode Switch(config)# hostname name Switch(config)# enable secret password Switch(config)# line console 0 Switch(config-line)# password password Switch(config-line)# login Switch(config)# line vty 0 4 Switch(config-line)# password password Switch(config-line)# login Switch(config)# banner motd # message # privilege password console password Switch(config)# interface type number Switch(config-if)# description description configure interface telnet password banner 28
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Navigating the IOS Navigating between IOS Modes (cont.) Switch> user mode Switch> enable go to privilege mode Switch# configure terminal go to global configuration mode Switch(config)# interface vlan 1 go to interface mode Switch(config-if)# exit Switch(config)# exit Switch# config t Shortened commands and parameters Switch(config)# vlan 1 go to VLAN configuration mode Switch(config-vlan)# end go to privilege-EXEC mode Switch# disable Switch> enable Switch# config t Switch(config)# line vty 0 4 go to interface (line) mode Switch(config-line)# exit Switch(config)# 27
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• Use a planner. Or use your cell or iPod calendar function. • Put aside time for studying. Really. Schedule time for studying. An Overview • Assume to have 2 to 3 hours of studying for hour you spend in class. – This may not be the case for every class, but is a good guideline. • Build a routine, find a time that works for you and try not to change it. – If you make studying a regular habit it will no longer seem as a chore. – Location is important, too. • Use small pieces of time. – Not all studying has to be done in hour long blocks. Even just fifteen or twenty minutes of focused studying can be very helpful. – Consider this…what do you ever do for four hours straight? – Shorter, focused, quality studying is better than lengthy, poor quality studying. • Do not procrastinate. – Deadlines can approach very quickly. Once you fall behind, it generally takes more work to catch up than originally studying would have required.
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