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Profiles and Majority Voting-Based Ensemble Method for protein secondary structure prediction

EVOLUTIONARY BIOINFORMATICS 2011年第7期7卷 171-188页

作者： Bouziane, Hafida Messabih, Belhadri Chouarfia, Abdallah USTO MB Univ Dept Comp Sci Oran Algeria

Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier.

关键词： protein secondary structure prediction k-Nearest Neighbors feed-forward Neural Networks Multi-class Support Vector Machines (M-SVMs) ensemble method Position-Specific Scoring Matrix (PSSM) profiles

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Estimating the Class Posterior Probabilities in protein secondary structure prediction

Estimating the Class Posterior Probabilities in Protein Seco...

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6th IAPR International Conference on Pattern Recognition in Bioinformatics

作者： Guermeur, Yann Thomarat, Fabienne LORIA Equipe ABC F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783642248542

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 class posterior probability estimates. This paper addresses the problem of post-processing the outputs of multi-class support vector machines used as sequence-to-structure classifiers with a structure-to-structure classifier estimating the class posterior probabilities. The aim of this comparative study is to obtain improved performance with respect to both criteria: prediction accuracy and quality of the estimates.

关键词： protein secondary structure prediction multi-class support vector machines class membership probabilities

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prediction of protein secondary structure using SVM-PSSM Classifier Combined by Sequence Features

Prediction of Protein Secondary Structure using SVM-PSSM Cla...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Yehong Chen Yihui Liu Jinyong Cheng Yanchun Wang Institute of Intelligent Information Processing Qilu University of Technology

ISBN: (纸本)9781509001668

protein secondary structure prediction is an important problem in *** this task,a method based on the SVM-PSSM Classifier combined by sequence feature（SF） is proposed in this *** sequence data is represented by a hybrid formation which combines the PositionSpecific Scoring Matrix（PSSM） with the Hydrophobicity Sequence Feature（HSF）,and the Structural Sequence Feature（SSF）.The novel feature formulations are proposed to extract more than 40 SFs（sequence features）,which further are examined for feature selection as accessional input vectors to SVM-PSSM classifier,to construct more accurate *** prediction classifier in this study is constructed using PSSM and selected sequence *** experimental results show that our final constructed SVM-PSSM-SF classifier achieved accuracy Q of 78%,that is higher than the traditional method which using the amino acid sequence as the data set.

关键词： protein secondary structure prediction SVM PSSM Sequence feature Amino acid scale

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Convolutional Neural Networks in Combination with Support Vector Machines for Complex Sequential Data Classification 27th

Convolutional Neural Networks in Combination with Support Ve...

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27th International Conference on Artificial Neural Networks (ICANN)

作者： Dionysiou, Antreas Agathocleous, Michalis Christodoulou, Chris Promponas, Vasilis Univ Cyprus Dept Comp Sci POB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Dept Biol Sci POB 20537 CY-1678 Nicosia Cyprus

ISBN: (纸本)9783030014216

Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. Deep Machine Learning techniques, such as Convolutional Neural Networks (CNNs), have been exclusively designed to face this class of problems. Support Vector Machines (SVMs) are a powerful technique for general classification problems, regression, and outlier detection. In this paper we present the development and implementation of an innovative by design combination of CNNs with SVMs as a solution to the protein secondary structure prediction problem, with a novel two dimensional (2D) input representation method, where Multiple Sequence Alignment profile vectors are placed one under another. This 2D input is used to train the CNNs achieving preliminary results of 80.40% per residue accuracy (Q3), which are expected to increase with the use of larger training datasets and more sophisticated ensemble methods.

关键词： Convolutional Neural Networks Support Vector Machines Deep learning Machine learning Bioinformatics protein secondary structure prediction

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Insertion of Epitope Tag into NB-LRR Class Plant Virus Resistance protein

Insertion of Epitope Tag into NB-LRR Class Plant Virus Resis...

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作者： Kappei Kobayashi Reiko Tomita Ken-Taro Sekine

It is prerequisite to detect plant disease resistance proteins for studying the function of the proteins. Numerous studies have used epitope tags fused to either N- or C-terminus for the detection of resistance proteins. However, some resistance proteins do not tolerate the terminal fusions of epitope tags. In this chapter, we provide a protocol for searching the protein regions in which the inserted epitope tag does not affect the protein function. In the protocol, we first perform an in silico search to select the insertion site candidates and then insert there a short sequence containing restriction sites to find out the sites, in which the insertion does not affect the protein function. Epitope tags are inserted into the experimentally selected sites to produce a functional protein with an epitope tag. less

关键词： protein structure Analysis PCR Infiltration protein secondary structure prediction Electrophoresis Electroporation DNA Ligation Escherichia coli Saccharomyces cerevisiae Nicotiana benthamiana Agrobacterium tumefaciens Agroinfiltration protein function Epitope tag Resistance protein

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SAAS:Short Amino Acid Sequence - A Promising protein secondary structure prediction Method of Single sequence

SAAS:Short Amino Acid Sequence - A Promising Protein Seconda...

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The 3rd Asia Pacific protein Association Conference in Conjunction with the 3rd Symposium of the Chinese protein Society

作者： Yuanpai Zhou Ray P.S.Han School of Mathematics and Computational Science Xiangtan University Xiangtan P.R.China Civil Construction Engineering Department Guangxi University of Technology Department of Advanced Materials and Nanotechnology Peking University

In statistical methods of prediction protein secondary structure,many people focus on single amino acid frequencies in - helices,-sheets,and so on,or the impact near amino acids on a amino acid forming a secondary *** the paper considers short sequence of amino acids(3,4,5 or 6 amino acids) as integer,and statistics short sequence's probability forming secondary ***,many researchers select low homologous sequences as statistical *** this paper select whole PDB

关键词： component short amino acid sequence protein secondary structure prediction statistical method

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A fast and efficient nearest neighbor method for protein secondary structure prediction

A fast and efficient nearest neighbor method for protein sec...

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2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

作者： Wei Yang Kuanquan Wang Wangmeng Zuo Biocomputing Research Centre School of Computer Science and Technology Harbin institute of Technology Harbin 150001 China

ISBN: (纸本)9781424488094

Using PSSM profiles, various machine learning methods have been successfully developed for protein secondary structure prediction. With the steady increase of protein structure data, the probability of having available homologous structural information of the protein in real prediction is now fairly high and will continue to increase. Therefore, how to effectively utilize the ever-growing protein structure data has become a huge challenge and opportunity. In this paper, we propose a novel nearest neighbor method, DPred, to use both homologous and non-homologous information for protein secondary structure prediction. On the dataset composed of new solved proteins, the method achieves the overall Q3 and SOV scores of 87.51％ and 86.50％, which is comparable with Porter_H and better than PROTEUS and CDM.

关键词： Nearest neighbor protein secondary structure prediction data mining bioinformatics

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protein secondary structure prediction with Bidirectional Recurrent Neural Nets: Can Weight Updating for Each Residue Enhance Performance?

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6th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Agathocleous, Michalis Christodoulou, Georgia Promponas, Vasilis Christodoulou, Chris Vassiliades, Vassilis Antoniou, Antonis Univ Cyprus Dept Comp Sci POB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Dept Sci Biol POB 20537 CY-1678 Nicosia Cyprus

ISBN: (纸本)9783642162381

Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is currently considered as one of the optimal computational neural network type architectures for addressing the problem. In this paper, we implement the same BRNN architecture, but we use a modified training procedure. More specifically, our aim is to identify the effect of the contribution of local versus global information, by varying the length of the segment on which the Recurrent Neural Networks operate for each residue position considered. For training the network, the backpropagation learning algorithm with an online training procedure is used, where the weight updates occur for every amino acid, as opposed to Baldi et al. [1], where the weight updates are applied after the presentation of the entire protein. Our results with a single BRNN are better than Baldi et al. [1] by three percentage points (Q3) and comparable to results of [1] when they use an ensemble of 6 BRNNs. In addition, our results improve even further when sequence-to-structure output is filtered in a post-processing step, with a novel Hidden Markov Model-based approach.

关键词： protein secondary structure prediction Bidirectional Recurrent Neural Networks Bioinformatics and Computational Biology

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Predicting the protein structure using random forest approach

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Procedia Computer Science 2018年 132卷 1654-1662页

作者： Charu Kathuria Deepti Mehrotra Navnit Kumar Misra Amity University Noida Uttar Pradesh 201313 India Brahmanand College The Mall Kanpur 208004 India

Predicting the secondary structure of proteins is a challenging task. A large variety approaches exist that include observation using equipment’s and theoretical evaluation, in which the optimal structure is determined. The secondary structure determines 3D tertiary structure of protein, on which features and functionalities of protein depend. This paper use classification technique, Random Forest to build a model which is able to determine structure of unknown proteins. The dataset included the amide frequencies of proteins whose structure is known. Machine learning model is developed that can predict the structure of protein that still need to be exploited. The accuracy of the model is determined using ROC curve. The results confirm the performance of the model constructed using amides dataset.

关键词： protein secondary structure prediction amide frequencies random forest ROC area under the curve

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prediction of HPV Risk Types from protein secondary structure 7

Prediction of HPV Risk Types from Protein Secondary Structur...

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7th International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Choudhary, Nishant Biswal, Bhawani Sankar Mohapatra, Anjali IIIT BHUBANESWAR Dept CSE Bhubaneswar Orissa India

ISBN: (纸本)9781509000760

Identification of high risk among viruses is crucial for interpreting its oncogenic mechanisms. Also it is gainful in developing ultra modern clinical tools for its diagnosis, prevention and treatment. prediction of the structure of a particular virus helps to classify the risk type of that virus. prediction methods based on text based classification do not provide satisfactory results as compared to sequence-based techniques. In particular, text-mining approaches only relay on the documentation procured from the studies. Hence insufficiency of the evidences for an unknown virus gives rise to classification issues. Therefore risk prediction of viruses from protein sequence and its structure is an effective alternative to text based classification. In this work, Machine learning techniques are used for risk type classification of Papillomaviruses which is having high potentials to cause cancer.

关键词： human papillomaviruses(HPVs) HMMs SVM kernel method protein secondary structure prediction

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