# A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
## 4 Results and Discussion
Although 3\({}_{10}\)-helices and b-bridges constitute short secondary structure segments that have some structural similarity to alpha-helix and beta-strand, they are classified as coil residues. Generally, prediction methods are more precise in the core of regular secondary structure segments than at the termini. In order to demonstrate the effectiveness of our ensemble model, we implemented our method to predict protein secondary structure for three independent test sets, data1199, CB513 and CASP203. We selected three widely used methods, single-sequence-based prediction method employing LSTM-BRNNs (SPIDER3) [18], JPred4 [31] and RaptorX [32] for the comparison with our method. These three methods used an iterative deep learning neural network, JNet algorithm, and deep convolutional neural fields Deep Convolutional Neural Fields (DeepCNF) to predict protein secondary structure. As mentioned above, the similarity between protein sequences in the test and training sets was quite low, to prevent overestimating the performance of our predictor. The Q\({}_{3}\) accuracy and the corresponding Q\({}_{H}\), Q\({}_{E}\) and Q\({}_{C}\) accuracies of secondary structure prediction at individual amino acid obtained from data1199 by the above mentioned methods are shown in Figure 3. The Q\({}_{3}\) accuracy of our ensemble model is 84.0% with corresponding Q\({}_{H}\), Q\({}_{E}\) and Q\({}_{C}\) accuracies of 86.7%, 79.2% and 84.1%, respectively. Compared to SPIDER3, JPred4 and RaptorX, our method achieved an improvement in the Q\({}_{3}\) accuracy of 0.7%, 4.7% and 2.5%, respectively. Table 2 reports the SOV scores and MCC measurements of secondary structure prediction at individual amino acid from data1199 by different methods. The SOV score of the ensemble model was 81.6% and SOV\({}_{H}\), SOV\({}_{E}\) and SOV\({}_{C}\) scores were 85.7%, 84.4% and 76.6%, respectively. Compared to SPIDER3, JPred4 and RaptorX, our method achieved an improvement in the SOV scores of 0.7%, 10.6%  
Figure 3: Q\({}_{3}\) Accuracy of ensemble model and the other three methods for data1199. and 5.0%, respectively, though it was 1.8% lower than SPIDER3 in SOV\({}_{\rm H}\). Compared to other methods, it achieved some improvements in C\({}_{\rm H},\)C\({}_{\rm E}\) and C\({}_{\rm C}\). The Q\({}_{3}\) accuracy and the corresponding Q\({}_{\rm H}\), Q\({}_{\rm E}\) and Q\({}_{\rm C}\) accuracies of secondary structure prediction at individual amino acid obtained from CB513 by above mentioned methods are shown in Figure 4. The Q\({}_{3}\) accuracy of the ensemble model was 83.5% with corresponding Q\({}_{\rm H}\), Q\({}_{\rm E}\) and Q\({}_{\rm C}\) accuracies of 85.5%, 80.1% and 82.6%, respectively. Compared to SPIDER3, \(|\)Pred4 and RaptorX, our method achieved an improvement in the Q\({}_{3}\) accuracy of 0.6%, 6.5% and 1.1%, respectively. Table 3 reports the SOV scores and MCC measurements of secondary structure prediction at individual amino acid from CB513 by different methods. The SOV score of our method was 80.5%, and SOV\({}_{\rm H}\), SOV\({}_{\rm E}\) and SOV\({}_{\rm C}\) scores were 84.7%, 83.2% and 75.8%, respectively. Compared to SPIDER3, JPred4 and RaptorX, our method achieved an improvement in the SOV scores of 2.6%, 9.6% and 4.0%, respectively. The three MCC measurements of our method for the test set were higher than those by other methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Accuracy Measures**} \\ \cline{2-9}  & **SOV (\%)** & **SOV\({}_{\rm H}\) (\%)** & **SOV\({}_{\rm E}\) (\%)** & **SOV\({}_{\rm C}\) (\%)** & **C\({}_{\rm H}\)** & **C\({}_{\rm E}\)** & **C\({}_{\rm C}\)** \\ \hline ensemble & 80.50 & 84.70 & 83.20 & 75.80 & 0.8052 & 0.7964 & 0.6942 \\ SPIDER3 & 77.90 & 83.50 & 79.40 & 72.70 & 0.7905 & 0.7518 & 0.6524 \\ JPred4 & 70.90 & 77.90 & 74.20 & 64.10 & 0.7236 & 0.6771 & 0.6291 \\ RaptorX & 76.50 & 81.00 & 80.40 & 71.20 & 0.7833 & 0.7675 & 0.6735 \\ \hline \hline \end{tabular}
\end{table}