The Experts below are selected from a list of 15687 Experts worldwide ranked by ideXlab platform
Geoffrey J. Barton - One of the best experts on this subject based on the ideXlab platform.
-
jpred4 a Protein Secondary Structure prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred Protein Secondary Structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for Secondary Structure prediction. In addition to Protein Secondary Structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) Secondary Structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.
-
JPred4: a Protein Secondary Structure prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred Protein Secondary Structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for Secondary Structure prediction. In addition to Protein Secondary Structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) Secondary Structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues
-
Protein Secondary Structure prediction.
Current Opinion in Structural Biology, 1995Co-Authors: Geoffrey J. BartonAbstract:Abstract The past year has seen consolidation of Protein Secondary Structure prediction methods. The advantages of prediction from an aligned family of Proteins have been highlighted by several accurate predictions made ‘blind’, before any X-ray or NMR Structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.
Pedro Larrañaga - One of the best experts on this subject based on the ideXlab platform.
-
Parallel stochastic search for Protein Secondary Structure Prediction
2004Co-Authors: Víctor Robles, María S. Pérez, Vanessa Herves, José M. Peña, Pedro LarrañagaAbstract: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 generation is based on the usage of evolutionary information. In this paper we propose the usage of naive Bayes and Interval Estimation Naive Bayes (IENB) -a new semi naive Bayes approach- as suitable third generation methods for Protein Secondary Structure Prediction (PSSP). One of the main stages of IENB is based on a heuristic optimization, carried out by estimation of distribution algorithms (EDAs). EDAs are non-deterministic, stochastic and heuristic search strategies that belong to the evolutionary computation approaches. These algorithms under complex problems, like Protein Secondary Structure Prediction, require intensive calculation. This paper also introduces a parallel variant of IENB called PIENB (Parallel Interval Estimation Naive Bayes).
-
PPAM - Parallel Stochastic Search for Protein Secondary Structure Prediction
Parallel Processing and Applied Mathematics, 2003Co-Authors: Víctor Robles, María S. Pérez, Vanessa Herves, José M. Peña, Pedro LarrañagaAbstract: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 generation is based on the usage of evolutionary information. In this paper we propose the usage of naive Bayes and Interval Estimation Naive Bayes (IENB) –a new semi naive Bayes approach– as suitable third generation methods for Protein Secondary Structure Prediction (PSSP). One of the main stages of IENB is based on a heuristic optimization, carried out by estimation of distribution algorithms (EDAs). EDAs are non-deterministic, stochastic and heuristic search strategies that belong to the evolutionary computation approaches. These algorithms under complex problems, like Protein Secondary Structure Prediction, require intensive calculation. This paper also introduces a parallel variant of IENB called PIENB (Parallel Interval Estimation Naive Bayes).
Alexey Drozdetskiy - One of the best experts on this subject based on the ideXlab platform.
-
jpred4 a Protein Secondary Structure prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred Protein Secondary Structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for Secondary Structure prediction. In addition to Protein Secondary Structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) Secondary Structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.
-
JPred4: a Protein Secondary Structure prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred Protein Secondary Structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for Secondary Structure prediction. In addition to Protein Secondary Structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) Secondary Structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues
Víctor Robles - One of the best experts on this subject based on the ideXlab platform.
-
Parallel stochastic search for Protein Secondary Structure Prediction
2004Co-Authors: Víctor Robles, María S. Pérez, Vanessa Herves, José M. Peña, Pedro LarrañagaAbstract: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 generation is based on the usage of evolutionary information. In this paper we propose the usage of naive Bayes and Interval Estimation Naive Bayes (IENB) -a new semi naive Bayes approach- as suitable third generation methods for Protein Secondary Structure Prediction (PSSP). One of the main stages of IENB is based on a heuristic optimization, carried out by estimation of distribution algorithms (EDAs). EDAs are non-deterministic, stochastic and heuristic search strategies that belong to the evolutionary computation approaches. These algorithms under complex problems, like Protein Secondary Structure Prediction, require intensive calculation. This paper also introduces a parallel variant of IENB called PIENB (Parallel Interval Estimation Naive Bayes).
-
PPAM - Parallel Stochastic Search for Protein Secondary Structure Prediction
Parallel Processing and Applied Mathematics, 2003Co-Authors: Víctor Robles, María S. Pérez, Vanessa Herves, José M. Peña, Pedro LarrañagaAbstract: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 generation is based on the usage of evolutionary information. In this paper we propose the usage of naive Bayes and Interval Estimation Naive Bayes (IENB) –a new semi naive Bayes approach– as suitable third generation methods for Protein Secondary Structure Prediction (PSSP). One of the main stages of IENB is based on a heuristic optimization, carried out by estimation of distribution algorithms (EDAs). EDAs are non-deterministic, stochastic and heuristic search strategies that belong to the evolutionary computation approaches. These algorithms under complex problems, like Protein Secondary Structure Prediction, require intensive calculation. This paper also introduces a parallel variant of IENB called PIENB (Parallel Interval Estimation Naive Bayes).
Lijun Wang - One of the best experts on this subject based on the ideXlab platform.
-
An novel method of Protein Secondary Structure prediction based on compound pyramid model
2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010Co-Authors: Bingru Yang, Yun Zhai, Bing An, Wu Qu, Lijun WangAbstract:In this paper, we propose a compound pyramid model to predict Protein Secondary Structure, where homology analysis and an improved support vector machine (SVM) technology are used for predicting Protein Secondary Structure. The homology analysis is based on BP network model which uses pair-wise sequence alignment, and SVM classification considers the physical and chemical properties of amino acids. We employed SVM multi-classification and homogenous analysis methods in integrative layer of compound pyramid model proposed by us. Result shows that the ensemble prediction model gets better results in our experiment compared with other methods.
-
Predicting Protein Secondary Structure Based on Compound Pyramid Model
2010 IEEE International Conference on Granular Computing, 2010Co-Authors: Bingru Yang, Lijun Wang, Wu Qu, Yun ZhaiAbstract:Biological processes have produced the ultimate intelligent system, and now we are trying to understand biology by building intelligent systems. Protein Secondary Structure prediction is essential for the tertiary Structure modeling, and it is the one of the major challenge of bioinformatics. In this paper, we proposed a new type of intelligent system to predict the Protein Secondary Structure, and it contain a Compound Pyramid Model (CPM) which is gradually enhanced, multi-layered. This model is composed of four independent coordination's layers by intelligent interfaces, synthesizes several methods. The model penetrates the whole domain knowledge, and the effective attributes are chosen by Causal Cellular Automata, and the high pure Structure database is constructed for training. An optimized accuracy (Q3) for the RS126 and CB513 dataset of 83.99% and 85.58%, respectively, could be obtained. And the CASP8's sequences, the results are found to be superior to those produced by other methods, such as PSIPRED,SSPRO,SAM-T02,PHD Expert, PROF, JPRED, and so on. The result shows that our method has strong generalization ability.
-
New Trend of Protein Secondary Structure Prediction
2009 International Symposium on Intelligent Ubiquitous Computing and Education, 2009Co-Authors: Yun Zhai, Bingru Yang, Lijun Wang, Bing AnAbstract:At first, this paper reviews the development history of the Protein Secondary Structure prediction. Some concerned Secondary Structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49% and RS126 with 82.79% respectively. In the end, this paper points out several possible trends in the Protein Secondary Structure prediction.
