Bidirectional Dynamics for Protein Secondary Structure Prediction
Sequence Learning - Paradigms, Algorithms, and Applications
Correlation of Amino Acid Physicochemical Properties with Protein Secondary Structure Conformation
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
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
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
PSSP with dynamic weighted kernel fusion based on SVM-PHGS
Knowledge-Based Systems
Improving protein secondary structure prediction using a multi-modal BP method
Computers in Biology and Medicine
A compact hybrid feature vector for an accurate secondary structure prediction
Information Sciences: an International Journal
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Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research. In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD*) process, a mixed-modal back propagation neural network (MMBP) module and so on. Testing against data sets of non-redundant protein sequences returned values for the Q"3 accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy. Availability: http://www.kdd.ustb.edu.cn/protein_Web/.