Predicting the secondary structure of protein is an important step towards obtaining its three dimensional structure and consequently its function. At present, the best predictors are based on machine learning techniques, in particular neural network architectures. We introduce a new architecture called Denoeux belief neural network (DBNN) for the prediction problem. DBNN uses reference pattern...

A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and convenience of the approach used, the results are found to be superior to those produced by other methods, including the popular PHD method according to our own benchmarking results and the results from the recent Crit...

The GOR program for predicting protein secondary structure is extended to include triple correlation. A score system for a residue pair to be at certain conformation state is derived from the conditional weight matrix describing amino acid frequencies at each position of a window flanking the pair under the condition for the pair to be at the fixed state. A program using this score system to pr...

The solution of binary classification problems using the Support Vector Machine (SVM) method has been well developed. Though multi-class classification is typically solved by combining several binary classifiers, recently, several multi-class methods that consider all classes at once have been proposed. However, these methods require resolving a much larger optimization problem and are applicab...

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (ML...

SUMMARY We have created the GOR V web server for protein secondary structure prediction. The GOR V algorithm combines information theory, Bayesian statistics and evolutionary information. In its fifth version, the GOR method reached (with the full jack-knife procedure) an accuracy of prediction Q3 of 73.5%. Although GOR V has been among the most successful methods, its online unavailability has...

Proteins are one of the most important parts of an organism because of their vital tasks. Consequently, it is necessary to know both the primary and secondary structure of a protein as closely related to its biological function. Artificial Neural Networks (ANNs) are a useful methodology for secondary structure prediction of proteins. In this study, a generalized regression neural network (GRNN)...

Considerable research effort has been devoted to predicting the secondary structure of proteins from their amino acid sequences. Despite the plethora of prediction techniques, present methods typically have 76% approximate level of accuracy on an average. Thus, there is a considerable room for improvement. We present here a novel automated approach for the secondary structure prediction based o...

Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning second...

Progress in the area of secondary structure prediction has been frustratingly slow[6]. The most accurate predictors at the moment are trained to predict one of three secondary structural states (helix, strand or coil) for each residue at position i using sequence information from a “window” of residues i ± 7. Information from more distant sequence positions should improve predictions further, s...