Evidence theoretic protein fold classification based on the concept of hyperfold

Kavousi, Kaveh; Sadeghi, Mehdi; Moshiri, Behzad; Araabi, Babak Nadjar; Moosavi-Movahedi, Ali Akbar

doi:10.1016/j.mbs.2012.07.001

Cited by 15 publications

(9 citation statements)

References 33 publications

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“…Finally, genetic algorithm is adopted to obtain weights for the outputs of the second layer to get the final result. Kavousi et al (2012) proposed the method upon which an unknown query protein is assigned to a hyperfold rather than a single fold. Each hyper_fold is a set of interlaced folds with a centroid fold and Dempster rule has been used to combine the results.…”

Section: Related Workmentioning

confidence: 99%

A two-layer classification framework for protein fold recognition

Aram

Charkari

2015

Journal of Theoretical Biology

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Section: Related Workmentioning

confidence: 99%

A two-layer classification framework for protein fold recognition

Aram

Charkari

2015

Journal of Theoretical Biology

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“…Features that capture significant global and local discriminatory information and the classification techniques that perform consistently with these extracted features have been used in the literature [2]. A wide range of classification techniques such as, artificial neural networks (ANN) [3], [4], [5], [6], [7], [8], meta classifiers [9], [10], [11], [12], [13], K-nearest neighbors [14], [15], [16], [17], [18] and support vector machines (SVM) [19], [20], [21], [22], [23], [24], [25], [26], [27] have been used for the PFR. Among the classifiers employed to tackle the PFR, using support vector machine have attained the best results [26], [27], [28], [29], [30], [31], [32].…”

Section: Introductionmentioning

confidence: 99%

“…Achieved results have shown that the most significant enhancement for the protein fold prediction accuracy has been achieved by relying on the feature extraction approaches rather than the classification techniques being used [4], [15], [19], [27], [28], [29], [39]. In most of the studies that addressed the PFR by feature extraction techniques, global discriminatory information has been represented using the composition of the amino acids feature group (the percentage of the occurrence of the amino acids along the protein sequence divided by the length of protein sequence [19], [22], [30]). However, it has been shown that this feature group is not able to adequately reveal global information as it is not able to capture information regarding the length of the protein sequence [39], [40] which was shown as effective feature for the PFR [33], [41].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the methods adopted to extract global discriminatory information, a wider range of methods were used to extract local discriminatory information for the PFR [42] such as, pseudo amino acid composition [14], [15], [22], [23], [43], cross covariance [28], auto covariance [28], [42], bi-gram [29], [39], and tri-gram [3]. Despite the significant local discriminatory information provided using these approaches, most of these methods produce large number of features as well as large amount of redundant features [14], [29] which makes them computationally expensive for large protein data banks (e.g., cross covariance and tri-gram [3], [28]).…”

Section: Introductionmentioning

confidence: 99%

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A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Dehzangi

Paliwal

Lyons

et al. 2014

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.

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“…During the last two decades, a wide range of classifiers such as, Bayesianbased learners [1], Artificial Neural Network (ANN) [2], Hidden Markov Model (HMM) [3], Meta classifiers [4,5], Support Vector Machine (SVM) [6][7][8] and ensemble methods [1,9,10] have been implemented and applied to this problem. Despite the crucial impact of the classification techniques used in solving this problem, the most important enhancements achieved were due to the attributes being selected and feature extraction methods being used [2,6,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Protein Fold Recognition Using an Overlapping Segmentation Approach and a Mixture of Feature Extraction Models

Dehzangi

Paliwal

Sharma

et al. 2013

Lecture Notes in Computer Science

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Protein Fold Recognition (PFR) is considered as a critical step towards the protein structure prediction problem. PFR has also a profound impact on protein function determination and drug design. Despite all the enhancements achieved by using pattern recognitionbased approaches in the protein fold recognition, it still remains unsolved and its prediction accuracy remains limited. In this study, we propose a new model based on the concept of mixture of physicochemical and evolutionary features. We then design and develop two novel overlapping segmented-based feature extraction methods. Our proposed methods capture more local and global discriminatory information than previously proposed approaches for this task. We investigate the impact of our novel approaches using the most promising attributes selected from a wide range of physicochemical-based attributes (117 attributes) which is also explored experimentally in this study. By using Support Vector Machine (SVM) our experimental results demonstrate a significant improvement (up to 5.7%) in the protein fold prediction accuracy compared to previously reported results found in the literature. 4 The experimental results achieved in this step for all five classifiers for EDD and TG benchmarks are available upon request. 5 Details about the attribute selection process as well as the list and references of all 117 physicochemical-based attributes are available upon request.

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Evidence theoretic protein fold classification based on the concept of hyperfold

Cited by 15 publications

References 33 publications

A two-layer classification framework for protein fold recognition

A two-layer classification framework for protein fold recognition

A Segmentation-Based Method to Extract Structural and Evolutionary Features for Protein Fold Recognition

Protein Fold Recognition Using an Overlapping Segmentation Approach and a Mixture of Feature Extraction Models

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