De novo structure determination of proteins is a significant research issue of bioinformatics. Biochemical procedures for protein structure determination are costly. Use of different pattern classification techniques are proved to ease this task. In this article, the secondary structure prediction task has been mapped into a three-class problem of pattern classification, where the classes are h...

Prediction of the secondary structure of a protein from its aminoacid sequence remains an important and difficult task. Up to this moment, three generations of Protein Secondary Structure Algorithms have been defined: The first generation is based on statistical information over single aminoacids, the second generation is based on windows of aminoacids –typically 11-21 aminoacids– and the third...

Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%-80.2% (77.9%-80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles m...

Most of the state-of-the-art methods for protein seconday structure prediction are complex combinations of discriminant models. They apply a local approach of the prediction which is known to induce a limit on the expected prediction accuracy. A priori, the use of generative models should make it possible to overcome this limitation. However, among the numerous hidden Markov models which have b...

Sequence alignment methods are very e ective for secondary structure prediction. However, they are only applicable when the similarity of the sequences is high enough. We previously reported that the extended sequence alignment method, which uses not only amino acid letters but also strings of amino acid letters representing motifs as comparing units, enabled us to nd common motifs even among t...

RAP aims to refine protein secondary structure prediction from one of famous prediction tools. Protein secondary structure prediction has been extensively discussed for almost 50 years and the machine learning is one of feasible methods for it with more than 70% accuracy. PSIPRED, PHD and PROF are well-known machine learning approaches and based on the three-state prediction: helix, strand, and...

This paper presents the use of neural networks for the prediction of protein Secondary Structure. We propose a pre-processing stage based on the method of Cascaded Nonlinear Components Analysis (CNLPCA), in order to get a dimensional reduction of the data which may consider its nonlinearity. Then, the reduced data are placed in predictor networks and its results are combined. For the verificati...

Proteins are key biological molecules with diverse functions. With newer technologies producing more data (genomics, proteomics) than can be annotated manually, in silico methods of predicting their structure and thereafter their function has been christened the Holy Grail of structural bioinformatics. Successful secondary structure prediction provides a starting point for direct tertiary struc...

Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers’ outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outp...