Results 271 to 280 of about 257,655 (321)

Evaluation of secondary structure predictions in proteins

Biochimica et Biophysica Acta (BBA) - Protein Structure, 1977
Data of 33 proteins are used to compare four methods which predict secondary structure from the amino acid sequence. The prediction of alpha-helices according to the histogram method of Argos et al. (Argos, P., Schwarz, J. and Schwarz, J. (1976) Biochim. Biophys.
openaire   +5 more sources

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks

J. Bioinform. Comput. Biol., 2018
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature ...
Yanbu Guo, Bingyi Wang, Weihua Li, Bei Yang   +3 more
semanticscholar   +1 more source

Improving protein secondary structure predictions by prediction fusion

Information Fusion, 2009
Protein secondary structure prediction is still a challenging problem at today. Even if a number of prediction methods have been presented in the literature, the various prediction tools that are available on-line produce results whose quality is not always fully satisfactory.
PALOPOLI, Luigi, S. ROMBO, TERRACINA, Giorgio, G. TRADIGO, P. VELTRI   +4 more
openaire   +3 more sources

Prediction of protein secondary structure at 80% accuracy

Proteins: Structure, Function, and Genetics, 2000
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
Morten Nielsen, Thomas Nordahl Petersen, Claus Lundegaard, Jakob Bohr, Henrik Bohr, Søren Brunak, Garry P. Gippert, Ole Lund   +7 more
openaire   +3 more sources

Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction

arXiv.org, 2017
Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we show how to adapt some of these techniques to create a novel chained convolutional architecture with ...
A. Busia, N. Jaitly
semanticscholar   +1 more source

HMM in predicting protein secondary structure

Wuhan University Journal of Natural Sciences, 2003
We introduced a new method—duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study, we divide the basic second structure of protein into three parts: H (α-Helix), E (β-sheet) and O (others, include coil and turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino
Zou Xiu-fen, Shi Feng, Li Yuanxiang, Zhou Huai-bei, Huang Jing   +4 more
openaire   +2 more sources

Study and Prediction of Secondary Structure for Membrane Proteins

Journal of Biomolecular Structure and Dynamics, 2007
In this paper we present a novel approach to membrane protein secondary structure prediction based on the statistical stepwise discriminant analysis method. A new aspect of our approach is the possibility to derive physical-chemical properties that may affect the formation of membrane protein secondary structure.
Ivan V. Filatov, Svetlana R. Amirova, Juri V. Milchevsky, Natalia G. Esipova, Vladimir G. Tumanyan   +4 more
openaire   +3 more sources

A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach.

Journal of Molecular Biology, 2001
We have introduced a new method of protein secondary structure prediction which is based on the theory of support vector machine (SVM). SVM represents a new approach to supervised pattern classification which has been successfully applied to a wide range
S. Hua, Z. Sun
semanticscholar   +1 more source

Protein secondary structure prediction with dihedral angles

Proteins: Structure, Function, and Bioinformatics, 2005
AbstractWe present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three‐state accuracy (Q3) of 79.4% in a cross‐validated trial on a nonredundant set of 513 proteins. An iterative set of cascade–correlation neural networks is used to predict both secondary structure and ψ dihedral angles, with predicted values ...
Jonathan D. Hirst, Matthew J. Wood
openaire   +3 more sources

The GOR Method for Predicting Secondary Structures in Proteins

1989
The widely used term “secondary structure” implies that it is of value to consider the structure of a protein as organized hierarchically. “Hierarchic” relates to the idea that the structure can be considered on at least two levels; there are, in fact, three levels of interest here, namely, the covalent structure (primary), the structural organization ...
Garnier, J., Robson, B.
openaire   +3 more sources