**8. Conclusion**

146 Recurrent Neural Networks and Soft Computing

Fig. 13. Performance comparison between BRNN-SVM and other protein domain prediction methods on two-domain. The best performance for two-domain prediction is BRNN-SVM with 73% for sensitivity and 76% for specificity since the secondary structure information has given a strong signal to assign protein boundaries because the protein secondary structure predicted is based on interaction between long-range interactions of the amino acid.

Fig. 14. Performance comparison between BRNN-SVM and other protein domain prediction methods on multiple-domain. The best performance of multiple-domain prediction is BRNN-SVM with 81% sensitivity and 79% specificity since the BRNN is a transaction between an input and an output sequence of variable length. This shows that interaction exists in protein

folding and plays an important role in the formation of protein secondary structure.

An algorithm named BRNN-SVM has been developed in order to solve the problem of weak domain signal. The algorithm begins with searching the seed protein sequences as dataset from SCOP 1.73. The dataset is split into training and testing sets. Then, multiple sequence alignment is performed prior to the prediction of protein secondary structure using BRNN. Several measures such as entropy, protein sequence termination, correlation, contact profile, physio-chemical properties and intron-exon data are used to increase the strength of domain signal from protein secondary structure. SVM classified the prediction into single-domain, two-domain and multiple-domain. Lastly, the results from SVM are evaluated in term of sensitivity and specificity. BRNN is based on forward, backward and hidden Markov chains that transmit information in both directions along the sequence between the input and output. Therefore, it increases accuracy of protein secondary prediction and well as providing strong domain signal from this protein secondary structure based on the generated measures. This is believed to be the reason why BRNN-SVM is a good method for protein domain predictors especially in twodomain and multiple-domain
