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International Conference on Computational Intelligence and Communication Networks

作者： Nishant Choudhary Bhawani Sankar Biswal Anjali Mohapatra Dept. of CSE IIIT Bhubaneswar Bhubaneswar India

ISBN: (纸本)9781509000777

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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prediction of secondary structures of proteins using a two-stage method

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COMPUTERS & CHEMICAL ENGINEERING 2008年第1-2期32卷 78-88页

作者： Yueksektepe, Fadime Ueney Yilmaz, Oezlem Tuerkay, Metin Koc Univ Coll Engn TR-34450 Istanbul Turkey

protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. However, prediction accuracies of these methods rarely exceed 70%. In this paper, a novel two-stage method to predict the location of secondary structure elements in a protein using the primary structure data only is presented. In the first stage of the proposed method, the folding type of a protein is determined using a novel classification approach for multi-class problems. The second stage of the method utilizes data available in the protein Data Bank and determines the possible location of secondary structure elements in a probabilistic search algorithm. It is shown that the average accuracy of the predictions is 74.1 % on a large structure dataset. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： protein secondary structure prediction data classification mixed-integer linear programming

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The influence of gapped positions in multiple sequence alignments on secondary structure prediction methods

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2004年第5-6期28卷 351-366页

作者： Simossis, VA Heringa, J Free Univ Amsterdam Fac Sci Bioinformat Sect NL-1081 HV Amsterdam Netherlands Free Univ Amsterdam IBIVU Fac Sci NL-1081 HV Amsterdam Netherlands Free Univ Amsterdam Fac Earth & Life Sci NL-1081 HV Amsterdam Netherlands

All currently leading protein secondary structure prediction methods use a multiple protein sequence alignment to predict the secondary structure of the top sequence. In most of these methods, prior to prediction, alignment positions showing a gap in the top sequence are deleted, consequently leading to shrinking of the alignment and loss of position-specific information. In this paper we investigate the effect of this removal of information on secondary structure prediction accuracy. To this end, we have designed SymSSP, an algorithm that post-processes the predicted secondary structure of all sequences in a multiple sequence alignment by (i) making use of the alignment's evolutionary information and (ii) re-introducing most of the information that would otherwise be lost. The post-processed information is then given to a new dynamic programming routine that produces an optimally segmented consensus secondary structure for each of the multiple alignment sequences. We have tested our method on the state-of-the-art secondary structure prediction methods PHD, PROFsec, SSPro2 and JNET using the HOMSTRAD database of reference alignments. Our consensus-deriving dynamic programming strategy is consistently better at improving the segmentation quality of the predictions compared to the commonly used majority voting technique. In addition, we have applied several weighting schemes from the literature to our novel consensus-deriving dynamic programming routine. Finally, we have investigated the level of noise introduced by prediction errors into the consensus and show that predictions of edges of helices and strands are half the time wrong for all the four tested prediction methods. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： protein secondary structure prediction prediction optimization local weighting optimal segmentation dynamic programming

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A compact hybrid feature vector for an accurate secondary structure prediction

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INFORMATION SCIENCES 2011年第23期181卷 5267-5277页

作者： Hassan, Rohayanti Othman, Razib M. Saad, Puteh Kasim, Shahreen Univ Teknol Malaysia Lab Computat Intelligence & Biotechnol Utm Skudai 81310 Malaysia Univ Teknol Malaysia Dept Software Engn Fac Comp Sci & Informat Syst Utm Skudai 81310 Malaysia Univ Tun Hussein Onn Malaysia Dept Informat Syst Fac Informat Technol & Multimedia Batu Pahat 86400 Malaysia

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Amino acid propensity score Dihedral angles Local protein structure protein secondary structure prediction Support vector machines

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FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy K-nearest neighbors with Lempel-Ziv complexity based distance measure

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NEUROCOMPUTING 2015年 148卷 409-419页

作者： Tan, Yong Tat Rosdi, Bakhtiar Affendi Univ Sains Malaysia Sch Elect & Elect Engn George Town 14300 Malaysia

Correct prediction of protein secondary structural classes is vital for the prediction of tertiary structures and understanding of their function. Most of the prediction algorithms require lengthy computation time. Nearest neighbor - complexity distance measure (NN-CDM) algorithm was one of the significant prediction algorithms using Lempel-Ziv (LZ) complexity-based distance measure, but it is slow and ineffective in handling uncertainties. To solve the problems, we propose fuzzy NN-CDM (FKNN-CDM) algorithm that incorporates the confidence level of prediction results and enhance the prediction process by designing hardware architecture that implements the proposed algorithm in an FPGA board. Highest average prediction accuracies for Z277 and 25PDB datasets using proposed algorithm are 84.12% and 47.81% respectively, with 15 times faster computation using an Altera DE2-115 FPGA board. (C) 2014 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction protein structural class Lempel-Ziv algorithm K-NN classification algorithm Fuzzy k-nearest-neighbor algorithm Field programmable gate arrays

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AMINO-ACID-SEQUENCE HOMOLOGY APPLIED TO THE prediction OF protein secondary structureS, AND JOINT prediction WITH EXISTING METHODS

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BIOCHIMICA ET BIOPHYSICA ACTA 1986年第1期871卷 45-54页

作者： NISHIKAWA, K OOI, T KYOTO UNIV INST CHEM RES UJI KYOTO 611 JAPAN

The assumption that homologous segments in different proteins may share a similar conformation is applied to the prediction of secondary structures in proteins. Sequences homologous to a target protein are searched, without allowing any gap, and compared against a number of reference proteins of known three-dimensional structure, and then a conformational state (.alpha., .beta. or coil) for each residue of the protein is predicted by looking at the secondary structure of corresponding homologous segments. This prediction is done in a statistical rather than deterministic way, by assigning the most probable conformation state among homologous data to each residue site of a target protein. A test application for 22 sample proteins yields 60% correctness on the average, a better value in comparison with two other existing methods. Joint prediction combining three methods into one is shown to increase the reliability up to 70%, when only the regions identically predicted with the three methods are taken into account. Application of the present method to 10 proteins of unknown structure is demonstrated.

关键词： protein secondary structure prediction Sequence homology Conformation homology Joint prediction method

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A prediction OF THE secondary structure OF THE PLECKSTRIN HOMOLOGY DOMAIN

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proteinS-structure FUNCTION AND BIOINFORMATICS 1994年第1期20卷 1-3页

作者： JENNY, TF BENNER, SA SWISS FED INST TECHNOL DEPT CHEM CH-8092 ZURICH SWITZERLAND

A consensus prediction for the secondary structure of the pleckstrin homology (PH) domain is presented. The prediction is based on an analysis of patterns of conservation and variation of homologous protein sequences. The structure is predicted to be formed largely from beta strands with a single alpha helix. (C) 1994 Wiley-Liss, Inc.

关键词： protein secondary structure prediction PLECKSTRIN HOMOLOGY DOMAIN

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WHEN AWAITING BIO-CHAMPOLLION - DYNAMIC-PROGRAMMING REGULARIZATION OF THE protein secondary structure predictionS

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protein ENGINEERING 1994年第10期7卷 1197-1202页

作者： ZIMMERMANN, K INRA UNITE INGN PROTF-78352 JOUY EN JOSASFRANCE

predictions of protein secondary structure using current methods are often unrealistic, i.e. the predicted alpha-helices or beta-strands are too short. To improve the realism, various heuristic 'filtering' or 'smoothing' methods are used. They are more or less intuitive and are based on ad hoc corrections. We present a regularization method to obtain a realistic secondary structure from predicted propensities. It is based on the known dynamic programming algorithm and is quite objective. It can be used with any prediction method which yields propensities. The regularized predictions conserve well the overall prediction accuracy and improve the 'protein-likeness' of the prediction.

关键词： DYNAMIC PROGRAMMING FILTERING MORPHOLOGICAL CONSTRAINTS protein secondary structure prediction REALISM REGULARIZATION

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Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimensional structures of proteins and its implications for secondary structure prediction from amino acid sequences

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JOURNAL OF BIOSCIENCES 2000年第1期25卷 81-91页

作者： Mugilan, SA Veluraja, K Manonmaniam Sundaranar Univ Dept Phys Tirunelveli 627012 India

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected nonhomologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for alpha-helix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For beta-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.

关键词： amino acid singlets doublets and triplets deviation parameters prediction accuracy protein secondary structure prediction

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A Resilient Voting Scheme for Improving secondary structure prediction

A Resilient Voting Scheme for Improving Secondary Structure ...

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5th Multi-Disciplinary International Workshop on Artificial Intelligence, MIWAI-2011

作者： Hota, Chittaranjan Ledda, Filippo Armano, Giuliano Dept Comp Sci BITS Pilani Hyderabad Campus Hyderabad Andhra Pradesh India Univ Cagliari Dept Elect & Comp Engn Cagliari Italy

ISBN: (纸本)9783642257247;9783642257254

This paper presents a novel approach called Resilient Voting Scheme (RVS), which combines different predictors (experts) with the goal of improving the overall accuracy. As combining multiple experts involves uncertainty and imprecise information, the proposed approach cancels out the impact of bad performers while computing a single collective prediction. RVS uses a genetic algorithm to assign a reliability to each expert, by using the Q3 measure as fitness function. A resilient voting is then used to improve the accuracy of the final prediction. RVS has been tested with well known datasets and has been compared with other state-of-the-art combination techniques (i.e., averaging and stacking). Experimental results demonstrate the validity of the approach.

关键词： Artificial Neural Networks Voting Resilient Protocols protein secondary structure prediction

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