The geometric opinion pool (GOP) ensemble method uses a multiplicative combination of predictors, and it is tailored to probability estimation in multi-class problems. This enables a decomposition of the KullbackLeibler entropy error function into an ambiguity term and an average error term. This can be used to estimate generalization error with a combination of cross-validation and estimation ...

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure ...

Protein Secondary Structure Prediction (PSSP) is considered as a challenging task in bioinformatics and so many approaches have been proposed in the literature to solve this problem via achieving more accurate prediction results. Accurate prediction of secondary structure is a critical role in deducing tertiary structure of proteins and their functions. Among the proposed approaches to tackle t...

The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset t...

Some properties of a protein can be determined from Knowing the secondary structure of the protein. The known structure of proteins so far was done using a technique called X-ray diffraction patterns of crystallized then the data from the process is fed through the DSSP algorithm(Kabsch and Sander, 1983) to determine the exact protein structure. The process is time consuming and expensive. Ther...

Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem. Common methods use feed forward neural networks or SVM’s combined with a sliding window, as these models does not naturally handle sequential data. Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data. We use a bidirect...

Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not c...

We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the pr...

In the present work, a novel method was proposed for prediction of secondary structure. Over a database of 396 proteins (CB396) with a three-state-defining secondary structure, this method with jackknife procedure achieved an accuracy of 68.8% and SOV score of 71.4% using single sequence and an accuracy of 73.7% and SOV score of 77.3% using multiple sequence alignments. Combination of this meth...

We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain secondary structure conformation withi...