A two-layer classification framework for protein fold recognition

Aram, Reza; Charkari, Nasrollah Moghadam

doi:10.1016/j.jtbi.2014.09.032

Cited by 13 publications

(2 citation statements)

References 39 publications

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“…Tests were performed on the dataset in [14] and 74.2% accuracy rate was obtained. Another up-to-date study was performed by Aram et al [27] in 2015. They used a two-layer classification framework (TLCF) and a fusion of MLP, RBFN (radial basis function network), and rotation forest.…”

Section: Introductionmentioning

confidence: 99%

Protein fold classification with Grow-and-Learn network

Polat¹,

Dokur²

2017

Turk J Elec Eng & Comp Sci

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Protein fold classification is an important subject in computational biology and a compelling work from the point of machine learning. To deal with such a challenging problem, in this study, we propose a solution method for the classification of protein folds using Grow-and-Learn (GAL) neural network together with one-versus-others (OvO) method. To classify the most common 27 protein folds, 125 dimensional data, constituted by the physicochemical properties of amino acids, are used. The study is conducted on a database including 694 proteins: 311 of these proteins are used for training and 383 of them for testing. Overall, the classification system achieves 81.2% fold recognition accuracy on the test set, where most of the proteins have less than 25% sequence identity with the ones used during the training. To portray the capabilities of the GAL network among the other methods, comparisons between a few approaches have also been made, and GAL's accuracy is found to be higher than those of the existing methods for protein fold classification.

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Section: Introductionmentioning

confidence: 99%

Protein fold classification with Grow-and-Learn network

Polat¹,

Dokur²

2017

Turk J Elec Eng & Comp Sci

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“…As the matter stands, there are two popular classes of computational methods for predicting the protein tertiary structure: initially, Template-Based Methods(TBM) and Ab initio methods. For identifying the tertiary structure of a given sequence, TBM may suggest taking advantage of the known three-dimensional structures from PDB as a template, which is world-wide protein structural database [3].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Protein Structure Classes

Wang

Bao

Han

et al. 2015

Intelligent Computing Theories and Methodologies

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Abstract. Prediction of protein structural are crucial in Bioinformatics. More and more evidences demonstrate that an great number of prediction methods has been employed to predict these structures based on the sequences of protein and biostatistics. The accuracy of such methods, nevertheless, is strongly affected by the efficiency and the robustness of classification model and other several factors. In our present research, the features based on the correlation coefficient of dipeptide or polypeptide were put forward. For one thing, flexible neutral tree (FNT), a novel classification model which is a variable structure neural network, is employed as the base classifiers. For another, the alterable tree structure based on FNT, such model may take advantage of the selection of available information, which aimed at the improvement of efficiency. It is important to find out the tree structural of protein structure classification model. To examine the performance of such method, ASTRAL, 1189 and 640 are selected as benchmark datasets of protein tertiary structure. Fortunately, the results show that a higher prediction accuracy compared with other methods. With the selected features running in the flexible neutral tree, several redundant information of features may be cut off and the accuracy of such model may be improved in some degree and the time of running such model could be hold down.

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Protein folds recognized by an intelligent predictor based‐on evolutionary and structural information

2015

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Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks incorporated in an adaptive heterogeneous particle swarm optimizer. The main contribution of NiRecor is that it is a data-driven and highly-performing predictor without manually tuning control parameters for different data sets. In biological science, since evolutionary- and structure-based information of amino acid sequences is greatly important in determination of tertiary structure of a protein, accordingly, in NiRecor we employ two different feature sets, which involve position specific scoring matrix and secondary structure prediction matrix, to predict the structural classes of protein folds. The experimental results demonstrate the proposed method is powerful in predicting protein folds with higher precisions by improvements of 1.1 ∼7.8 percentages on three benchmark datasets by comparing with several existing predictors.

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A two-layer classification framework for protein fold recognition

Cited by 13 publications

References 39 publications

Protein fold classification with Grow-and-Learn network

Protein fold classification with Grow-and-Learn network

Prediction of Protein Structure Classes

Protein folds recognized by an intelligent predictor based‐on evolutionary and structural information

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