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The prediction of protein secondary structure with a cascade correlation learning architecture of neural networks

NEURAL COMPUTING & APPLICATIONS 1997年第1期6卷 57-62页

作者： Vivarelli, F Fariselli, P Casadio, R UNIV BOLOGNA DEPT BIOL BIOPHYS LAB I-40126 BOLOGNA ITALY

A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards rite global minimum of the error function more efficiently than BPNN. However;only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (C-alpha, and C-beta and C-coil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system.

关键词： cascade correlation learning algorithm neural networks pattern recognition predictive methods protein secondary structure prediction

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prediction OF protein secondary structure BY COMBINING NEAREST-NEIGHBOR ALGORITHMS AND MULTIPLE SEQUENCE ALIGNMENTS

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JOURNAL OF MOLECULAR BIOLOGY 1995年第1期247卷 11-15页

作者： SALAMOV, AA SOLOVYEV, VV BAYLOR COLL MED DEPT CELL BIOLHOUSTONTX 77030

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

关键词： protein secondary structure prediction NEAREST-NEIGHBOR ALGORITHM LOCAL STRUCTURAL ENVIRONMENT SCORING MULTIPLE SEQUENCE ALIGNMENT EVOLUTIONARY INFORMATION

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prediction of protein secondary structure with a reliability score estimated by local sequence clustering

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protein ENGINEERING 2003年第9期16卷 651-657页

作者： Jiang, F Chinese Acad Sci Inst Phys Beijing 100080 Peoples R China

Most algorithms for protein secondary structure prediction are based on machine learning techniques, e.g. neural networks. Good architectures and learning methods have improved the performance continuously. The introduction of profile methods, e.g. PSI-BLAST, has been a major breakthrough in increasing the prediction accuracy to close to 80%. In this paper, a brute-force algorithm is proposed and the reliability of each prediction is estimated by a z-score based on local sequence clustering. This algorithm is intended to perform well for those secondary structures in a protein whose formation is mainly dominated by the neighboring sequences and short-range interactions. A reliability z-score has been defined to estimate the goodness of a putative cluster found for a query sequence in a database. The database for prediction was constructed by experimentally determined, non-redundant protein structures with <25% sequence homology, a list maintained by PDBSELECT. Our test results have shown that this new algorithm, belonging to what is known as nearest neighbor methods, performed very well within the expectation of previous methods and that the reliability z-score as defined was correlated with the reliability of prediction. This led to the possibility of making very accurate predictions for a few selected residues in a protein with an accuracy measure of Q(3) > 80%. The further development of this algorithm, and a nucleation mechanism for protein folding are suggested.

关键词： nearest neighbor nucleation protein folding protein secondary structure prediction sequence clustering stability

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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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prediction of protein secondary structure based on residue pairs

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Journal of Bioinformatics and Computational Biology 2004年第2期2卷 343-352页

作者： Liu, Xin Zhang, Li-Mei Zheng, Wei-Mou Interdisciplinary Ctr. Theor. Studs. Chinese Academy of Sciences Beijing 100080 China School of Science Beijing Jiaotong University Beijing 100044 China Institute of Theoretical Physics Beijing 100080 China

The GOR program for predicting protein secondary structure is extended to include triple correlation. A score system for a residue pair to be at certain conformation state is derived from the conditional weight matrix describing amino acid frequencies at each position of a window flanking the pair under the condition for the pair to be at the fixed state. A program using this score system to predict protein secondary structure is established. After training the model with a learning set created from PDB-SELECT, the program is tested with two test sets. As a method using single sequence for predicting secondary structures, the approach achieves a high accuracy near 70%. © Imperial College Press.

关键词： Amino acid triple correlation protein secondary structure prediction

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The prediction of protein secondary structure based on auto encoder 13

The prediction of protein secondary structure based on auto ...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Zhang Shuai-yan Liu Yi-hui Cheng Jin-yong Qilu Univ Technol Sch Informat Jinan 250353 Shandong Peoples R China

ISBN: (纸本)9781538621653

In this paper, a new encoding method composed of radical group encoding and position specific scoring matrix(PSSM) is proposed. The radical group encoding contains 42 features, it is formed according to amino acids composition. To combine the radical group encoding with the evolution matrix Blosum62, this will form a new encoding method. The Bayes classifier, single-layer auto encoder and stacked auto encoder with two hidden layers are used to predict the secondary structure. The auto encoder is mainly used to extract features, the single-layer auto encoder extracts 1500 features. The stacked auto encoder extracts features in two layers, in the first layer, it extracts 1500 features, in the second layer, it extracts 800 features. The accuracies will be compared. According to the comparision of the accuracies, the accuracy of stacked auto encoder is higher 1.39% than the accuracy of single-layer auto encoder. The accuracy of single-layer auto encoder is higher 1.23% than Bayes classifier.

关键词： protein secondary structure prediction radical group encoding PSSM Bayes classifier auto encoder

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A Hybrid Method for prediction of protein secondary structure Based on Multiple Artificial Neural Networks 40

A Hybrid Method for Prediction of Protein Secondary Structur...

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40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Hasic, Haris Buza, Emir Akagic, Amila Univ Sarajevo Dept Comp Sci & Informat Fac Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330922

The prediction of protein secondary structure is the method of finding the way in which an amino acid sequence causes the protein structure to fold and bend into alpha helices, beta strands and other shapes. Until today, the problem of finding protein secondary structure is not fully resolved. Classification or clusterization based methods have an accuracy rate of circa 80 percent and they mainly work on a reduced set of shapes and folds. It is very difficult to predict how a local sequence of amino acids is going to behave and in which way it is going to affect the future of protein structure. Based upon the predicted secondary structure of the protein, the tertiary and quaternary predictions show the real nature and function of the protein as a whole. In this paper, we address the problem of the secondary structure prediction of protein and propose a new hybrid method based on the usage of multiple neural networks with the use of a consensus function and compare our approach with other efficient methods.

关键词： Bioinformatics protein secondary structure prediction Hybrid Method Neural Networks Machine Learning

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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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prediction of protein secondary structure using Binary Classification Trees, Naive Bayes Classifiers and the Logistic Regression Classifier

Prediction of Protein Secondary Structure using Binary Class...

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作者： Ahmed Abdelkarim Eldud Omer RHODES UNIVERSITY

学位级别：硕士

The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix against not helix (H/∼H), sheet against not sheet (S/∼S) and coil against not coil (C/∼C). The performance of these binary classifiers are compared using the overall accuracy in predicting the protein secondary structure for various window sizes. Our result indicate that hold-out cross validation achieved higher accuracy than 5-fold cross validation. The Naive Bayes classifier, using 5-fold cross validation achieved, the lowest accu- racy for predicting helix against not helix. The classification tree classifiers, using 5-fold cross validation, achieved the lowest accuracies for both coil against not coil and sheet against not sheet classifications. The logistic regression classier accuracy is dependent on the window size; there is a positive relationship between the accuracy and window size. The logistic regression classier approach achieved the highest accuracy when compared to the classification tree and Naive Bayes classifiers for each classification task; predicting helix against not helix with accu- racy 77.74%, for sheet against not sheet with accuracy 81.22% and for coil against not coil with accuracy 73.39%. It is noted that it is easier to compare classifiers if the classification process could be completely facilitated in R. Alternatively, it would be easier to assess these logistic regression classifiers if SPSS had a function to determine the accuracy of the logis

关键词： Classification tree Naive Bayes logistic regression hold-out 5-fold cross validation protein secondary structure prediction

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prediction of protein secondary structure based on an improved channel attention and multiscale convolution module

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2022年 10卷 901018页

作者： Jin, Xin Guo, Lin Jiang, Qian Wu, Nan Yao, Shaowen Yunnan Univ Engn Res Ctr Cyberspace Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China

prediction of the protein secondary structure is a key issue in protein science. protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.

关键词： deep learning generative adversarial networks channel attention protein secondary structure prediction neural networks protein structure prediction

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