Expert Systems with Applications: An International Journal
An improved CBA prediction algorithm in compound pyramid model
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Expert Systems with Applications: An International Journal
A proposed knowledge based approach for solving proteomics issues
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Predicting protein second structure using a novel hybrid method
Expert Systems with Applications: An International Journal
Finding regularity in protein secondary structures using a cluster-based genetic algorithm
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
A resilient voting scheme for improving secondary structure prediction
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Exploiting Intrastructure Information for Secondary Structure Prediction with Multifaceted Pipelines
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving protein secondary structure prediction using a multi-modal BP method
Computers in Biology and Medicine
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Motivation: In our previous approach, we proposed a hybrid method for protein secondary structure prediction called HYPROSP, which combined our proposed knowledge-based prediction algorithm PROSP and PSIPRED. The knowledge base constructed for PROSP contains small peptides together with their secondary structural information. The hybrid strategy of HYPROSP uses a global quantitative measure, match rate, to determine whether PROSP or PSIPRED is to be used for the prediction of a target protein. HYPROSP made slight improvement of Q3 over PSIPRED because PROSP predicted well for proteins with match rate 80%. As the portion of proteins with match rate 80% is quite small and as the performance of PSIPRED also improves, the advantage of HYPROSP is diluted. To overcome this limitation and further improve the hybrid prediction method, we present in this paper a new hybrid strategy HYPROSP II that is based on a new quantitative measure called local match rate. Results: Local match rate indicates the amount of structural information that each amino acid can extract from the knowledge base. With the local match rate, we are able to define a confidence level of the PROSP prediction results for each amino acid. Our new hybrid approach, HYPROSP II, is proposed as follows: for each amino acid in a target protein, we combine the prediction results of PROSP and PSIPRED using a hybrid function defined on their respective confidence levels. Two datasets in nrDSSP and EVA are used to perform a 10-fold cross validation. The average Q3 of HYPROSP II is 81.8% and 80.7% on nrDSSP and EVA datasets, respectively, which is 2.0% and 1.1% better than that of PSIPRED. For local structures with match rate 80%, the average Q3 improvement is 4.4% on the nrDSSP dataset. The use of local match rate improves the accuracy better than global match rate. There has been a long history of attempts to improve secondary structure prediction. We believe that HYPROSP II has greatly utilized the power of peptide knowledge base and raised the prediction accuracy to a new high. The method we developed in this paper could have a profound effect on the general use of knowledge base techniques for various predictionalgorithms. Availability: The Linux executable file of HYPROSP II, as well as both nrDSSP and EVA datasets can be downloaded from http://bioinformatics.iis.sinica.edu.tw/HYPROSPII/ Contact: hsu@iis.sinica.edu.tw