Variable predictive model based classification algorithm for effective separation of protein structural classes

A NOVEL APPROACH FOR THE PATTERN RECOGNITION OF HAND MOVEMENTS BASED ON EMG AND VPMCD

Essentially, the classification of human hand movements is a process of pattern recognition. However, existing computationally intense and complex pattern recognition methods have failed thus far to be optimally successful in constructing associations between extracted signal features. Due to such limitations, a new pattern recognition method using variable predictive model-based class discrimination (VPMCD) is proposed. This approach considers that the feature values can exhibit inter-relations in nature and such associations will show different forms in different classes. In practice, this is always true for different hand movements. The signals produced by electromyography (EMG) and received from human arm muscles, are characteristically non-linear and non-stationary. A novel hand gesture recognition technique, based on wavelet feature extraction and VPMCD is proposed. First, the maximum values of the wavelet coefficient are extracted as the feature vectors from the surface EMG signals after de-noising. Then, the feature values are regarded as the inputs of the VPMCD classifier. Finally, four movement patterns (hand clenching, hand extension, wrist flexion, and wrist extension) are identified by the outputs of the VPMCD classifier. Our analysis results show that the proposed pattern recognition approach can distinguish different gestures successfully and effectively. Simultaneously, compared with the artificial neural network and the support vector machine classifier, more accurate recognition can be achieved using our proposed technique.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Prediction of protein structural class using a complexity-based distance measure

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.