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Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks
2009
2009 International Conference of Soft Computing and Pattern Recognition
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Protein fold pattern recognition has been one of the most challenging problems in biology during the last 40 years. ...
different protein fold classes. ...
Our experiments show that the use of Bayesian ensemble method is very promising in the problem of protein fold recognition because of decreasing both bias and variance error of the base classifiers in ...
doi:10.1109/socpar.2009.91
dblp:conf/socpar/HashemiSN09
fatcat:34awm2regzditdc7nik7jhmqry
A novel fusion based on the evolutionary features for protein fold recognition using support vector machines
2020
Scientific Reports
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Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. ...
Some of these approaches are based on extracting features from protein sequences and using a strong classifier. ...
Boosting classifier may be employed to find better solutions for protein fold recognition. Figure 1 . 1 Illustrates the framework of proposed protein fold recognition method. ...
doi:10.1038/s41598-020-71172-x
pmid:32873824
fatcat:2gbvq47dxjavjjegj6qkq3j2ya
An improved protein fold recognition with support vector machines
2010
Expert systems
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Protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. ...
Some of these approaches are based on extracting features from protein sequences and using a strong classifier. ...
When we study methods of protein fold recognition, we found that less attention has been paid to the fusion of features to get more comprehensive features. ...
doi:10.1111/j.1468-0394.2010.00572.x
fatcat:576zu4yrtbakdahkditv2csypu
Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction
2007
IEEE Transactions on Nanobioscience
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has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. ...
Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure. ...
The diversity rank/score graph for each pair of features in {B,F,G,H} for classifying protein folding patterns in class1; in {G,H} for classifying protein folding patterns in class2; in {B,D,G,H} for classifying ...
doi:10.1109/tnb.2007.897482
pmid:17695755
fatcat:xnpcwxczlbhvrohwlmxn5rtuwe
Protein classification using texture descriptors extracted from the protein backbone image
2010
Journal of Theoretical Biology
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The experimental results, validated using three different datasets (protein fold recognition, DNA-binding proteins recognition, biological processes and molecular functions recognition) along with different ...
In this work we propose a method for protein classification that combines different texture descriptors extracted from the 2-D distance matrix obtained from the 3-D tertiary structure of a given protein ...
Protein fold recognition (FOLD) The fold database used in our experiments is derived from the work of (Ding and Dubchak, 2001 ). ...
doi:10.1016/j.jtbi.2010.03.020
pmid:20307550
fatcat:rzxwjnb44beq3orox7wymh3vhe
Protein fold recognition using geometric kernel data fusion
2014
Computer applications in the biosciences : CABIOS
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We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. ...
The best overall accuracy on the protein fold recognition test set obtained by our methods is $86.7%. This is an improvement over the results of the best existing approach. ...
The architecture of our fusion model for protein fold recognition. ...
doi:10.1093/bioinformatics/btu118
pmid:24590441
pmcid:PMC4071197
fatcat:opcpnssq3jemfoqyptjdyagkfm
Protein Fold Recognition using a Structural Hidden Markov Model
2006
18th International Conference on Pattern Recognition (ICPR'06)
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Protein fold recognition has been the focus of computational biologists for many years. ...
We show how the concept of SHMM can efficiently use the protein secondary structure during the fold recognition task. ...
We have applied the concept of SHMM in order to exploit the relations between the secondary structures of a protein. This information is vital for the recognition of a protein 3D fold. ...
doi:10.1109/icpr.2006.949
dblp:conf/icpr/BouchaffraT06
fatcat:iii4ml53enba3mbiatqg5oispy
Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information
2014
BioMed Research International
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The recognition of protein folds is an important step for the prediction of protein structure and function. ...
After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (30960090, 31260203), The "CHUN HUI" Plan of Ministry of Education, and Talent Development Foundation of Inner ...
doi:10.1155/2014/262850
pmid:25136571
pmcid:PMC4127253
fatcat:o2v2kcjfqnaxvk7brmrpme75y4
A Tri-Gram Based Feature Extraction Technique Using Linear Probabilities of Position Specific Scoring Matrix for Protein Fold Recognition
2014
IEEE Transactions on Nanobioscience
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The proposed technique exhibits up to 4.4% improvement in protein fold recognition accuracy compared to the state-of-the-art feature extraction techniques. ...
The identification of protein folds from primary protein sequences is an intermediate step in discovering the three dimensional structure of a protein. ...
The prime objective of protein fold recognition is to find the fold of a protein sequence. ...
doi:10.1109/tnb.2013.2296050
pmid:24594513
fatcat:6lj52u3bk5a4lnndr4uppzxunu
Improved method for predicting protein fold patterns with ensemble classifiers
2012
Genetics and Molecular Research
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Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. ...
In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. ...
Figure 2 . 2 Comparison of six protein fold pattern recognition methods.
Table 1. Performance of different classifiers on protein fold pattern recognition (188-D features). ...
doi:10.4238/2012.january.27.4
pmid:22370884
fatcat:dvvjoiwfgjhxjd6sg7pgdwsk54
Protein Fold Recognition Using Genetic Algorithm Optimized Voting Scheme and Profile Bigram
2016
Journal of Software
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Several researchers have used PSSM for improving protein fold recognition and some of these include auto cross-covariance [18], bi-gram [19], 756 ...
Recently in protein fold recognition, the use of evolutionary features have been showing good performance [16] , [17] . ...
Introduction In the field of biological science, protein fold recognition refers to assigning a protein to one of a finite number of folds. ...
doi:10.17706/jsw.11.8.756-767
fatcat:i6ugc3k3fjadzj75micg7dslqu
Novel Machine Learning Techniques for Micro-Array Data Classification
[chapter]
2011
Bioinformatics - Trends and Methodologies
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Machine learning is also applied for protein function prediction, fold recognition as well as other relevant proteomics problems (Valentini, 2008) . ...
For the ensembles that use a SVM for fusion, the dataset is divided into a training set, validation set and a test set using k-fold cross validation. ...
doi:10.5772/22132
fatcat:4qn4ivx3azfgfmoherl6wp4qnq
Genetic algorithm for an optimized weighted voting scheme incorporating k-separated bigram transition probabilities to improve protein fold recognition
2014
Asia-Pacific World Congress on Computer Science and Engineering
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A set of SVM classifiers are used for initial classification, whereupon their predictions are consolidated using the optimized weighted voting system. ...
In this study, a scheme is proposed that uses the genetic algorithm (GA) to optimize a weighted voting system to improve protein fold recognition. ...
INTRODUCTION In the field of biological science, protein fold recognition refers to assigning a protein to one of a finite number of folds. ...
doi:10.1109/apwccse.2014.7053846
fatcat:k3qh3rfi5vaj3di46a2x56xqfy
Generation of new protein functions by nonhomologous combinations and rearrangements of domains and modules
2009
Current Opinion in Biotechnology
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Generation of novel protein functions is a major goal in biotechnology and also a rigorous test for our understanding of the relationship between protein structure and function. ...
, is highly effective in producing complex and sophisticated functions both in terms of molecular recognition and regulation. ...
Yan for critical reading of the manuscript. SK was supported by National Institutes of Health grants. ...
doi:10.1016/j.copbio.2009.07.007
pmid:19700302
pmcid:PMC2763956
fatcat:tuuryiagizfbtbd3qwoeksk2ba
Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA
2015
International Journal of Molecular Sciences
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An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. ...
The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing ...
Acknowledgments: This research is fully supported by grants from National Natural Science Foundation of China (11261068, 11171293). ...
doi:10.3390/ijms161226237
pmid:26703574
pmcid:PMC4691178
fatcat:26kksr4wgfcpvoq6ughtftycwu
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