检索结果-内蒙古大学图书馆

2010 Second Asia-Pacific Conference on Information Processing (APCIP 2010)

作者： Jun Guo,Fuming Lin,Su Wang,Xiaoping Liu,and Youguang Chen Computer Center East China Normal University 3663 Zhong Shan Rd.N.,Shanghai,China

A new coding scheme for protein secondary structure prediction is presented in this *** data set is first encoded by the pair-coupled amino acid *** then the conformation propensity factors are *** reencoded data set is trained by support vector machine(SVM).In test phase,the samples are encoded three times respectively,and a Max Wins algorithm is used to determine the final *** experimental results on the CB513 data set show the proposed method is one of excellent methods for protein secondary structure prediction.

关键词： protein secondary structure prediction conformation propensity factors support vector machine coding scheme

来源：评论

学校读者我要写书评

暂无评论

PCI-SS: WEB-BASED HUMAN AND MACHINE INTERFACES FOR protein secondary structure prediction

PCI-SS: WEB-BASED HUMAN AND MACHINE INTERFACES FOR PROTEIN S...

引用

2008 Canadian Conference on Electrical and Computer Engineering (CCECE 2008), vol.4

作者： Mohammed Aboul-Magd James R. Green Systems and Computer Engineering Carleton University Canada

PCI-SS is a web-based protein secondary structure prediction service that makes use of novel dynamic nonlinear system identification techniques. When used to combine multiple prediction systems, PCI-SS exhibits state-of-the-art prediction accuracy while reducing confusion of helix and strand structures, a particularly detrimental class of error, by up to 25%. Unlike other available secondary structure prediction methods, PCI-SS provides both human-readable and machine-readable web interfaces. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This facilitates incorporation of PCI-SS into systems biology analysis pipelines that require protein secondary structure information, and greatly simplifies high-throughput analyses. WSDL is used to precisely define the SOAP interface. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service.

关键词： protein secondary structure prediction PCI SOAP

来源：评论

学校读者我要写书评

暂无评论

An efficient Method for protein secondary structure prediction

An efficient Method for Protein Secondary Structure Predicti...

引用

The 1st International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Tao WU Junjun MAO Ling ZHANG State Key Laboratory for Novel Software Technology Department of Computer Science & TechnologyNanj School of Mathematics and Computational Science Anhui University Hefei 230039 R.P.China Key Lab IC&SP Anhui University Hefei 230039 R.P.China

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper,a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes,the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result.

关键词： protein secondary structure prediction constructive machine learning spherical neighborhood encoding scheme

来源：评论

学校读者我要写书评

暂无评论

IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction (vol 89, 1277, 2021)

引用

proteinS-structure FUNCTION AND BIOINFORMATICS 2022年第8期90卷 1613-1613页

作者： Gormez, Yasin Sabzekar, Mostafa Aydin, Zafer

来源：评论

学校读者我要写书评

暂无评论

protein secondary structure prediction USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2006年第4期6卷 551-567页

作者： Gubbi, Jayavardhana Lai, Daniel T. H. Palaniswami, Marimuthu Parker, Michael Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia St Vincents Inst Med Res Fitzroy Vic 3065 Australia

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou-Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q(3) accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on http://***/ISSNIP/bioinf/.

关键词： protein secondary structure prediction support vector machines position specific scoring matrix (PSSM) Chou-Fasman parameters Kyte-Dolittle hydrophobicity Grantham polarity reliability index novel encoding scheme

来源：评论

学校读者我要写书评

暂无评论

MUPRED: A tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction

引用

proteinS-structure FUNCTION AND BIOINFORMATICS 2007年第3期66卷 664-670页

作者： Bondugula, Rajkumar Xu, Dong Univ Missouri Christopher S Bond Life Sci Ctr 271C Digital Biol Lab Dept Comp Sci Columbia MO 65211 USA

Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q(3)) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q(3) can be achieved if a query protein has a significant sequence identity (> 25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://***/mupred. proteins 2007;66:664-670. (c) 2006 Wiley-Liss, Inc.

关键词： protein secondary structure prediction fuzzy nearest neighbor neural network hybrid prediction system sequence profile template prediction accuracy assessment

来源：评论

学校读者我要写书评

暂无评论

A Deep Convolutional Neural Network to Improve the prediction of protein secondary structure

引用

CURRENT BIOINFORMATICS 2020年第7期15卷 767-777页

作者： Guo, Lin Jiang, Qian Jin, Xin Liu, Lin Zhou, Wei Yao, Shaowen Wu, Min Wang, Yun Yunnan Univ Sch Software 2 Cuihu Rd Kunming Yunnan Peoples R China Yunnan Normal Univ Sch Informat Kunming Yunnan Peoples R China

Background: protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy. Objective: To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure. Methods: In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function. Results: Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%. Conclusion: We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.

关键词： Convolutional neural network deep learning evolutionary information multiple features molecular structure prediction protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for prediction of protein secondary structure

引用

Computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

prediction OF protein secondary structure AT BETTER THAN 70-PERCENT ACCURACY

引用

JOURNAL OF MOLECULAR BIOLOGY 1993年第2期232卷 584-599页

作者： ROST, B SANDER, C European Molecular Biology Laboratory Meyerhofstra&szlig e 1 D-6900 Heidelberg Germany

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 prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70·8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70·2%. The prediction is well balanced between α-helix, β-strand and loop: 65% of the observed strand residues are predicted correctly. The accuray in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server.

关键词： protein secondary structure prediction MULTIPLE SEQUENCE ALIGNMENTS secondary structure CONTENT NEURAL NETWORK

来源：评论

学校读者我要写书评

暂无评论

prediction of protein secondary structure Using Feature Selection and Analysis Approach

引用

ACTA BIOTHEORETICA 2014年第1期62卷 1-14页

作者： Feng, Yonge Lin, Hao Luo, Liaofu Inner Mongolia Agr Univ Coll Sci Hohhot 010018 Peoples R China Univ Elect Sci & Technol China Key Lab NeuroInformat Minist Educ Ctr BioinformatSch Life Sci & Technol Chengdu 610054 Peoples R China Inner Mongolia Univ Sch Phys Sci & Technol Hohhot 010021 Peoples R China

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80 % currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25 % was used to train and test the proposed method. The results indicate that overall accuracy of 87.8 % was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89 % at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.

关键词： protein secondary structure prediction Confidence level Tetrapeptide structural words Increment of diversity Quadratic discriminant analysis Error allowed scope

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：