Chaos Game Representation for Discriminating Thermophilic from Mesophilic Protein Sequences

Hu, Xuehai; Xia, Jingbo; Niu, Xiaohui; Ma, Xuan; Song, Chaohong; Shi, Feng

doi:10.1109/icbbe.2009.5162487

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…For our updated new data here, for the 24-dimensional vector, by using genetic algorithm, we find C = 578.524063; = 7.822955 be the best one and we get ACC value is 0.8792 and MCC is 0.7587. These results are very similar with [66].…”

Section: B Results With Svmsupporting

confidence: 94%

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Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Lu¹,

Hu²

2011

PPL

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Comprehensive knowledge of thermophilic mechanisms about some organisms whose optimum growth temperature (OGT) ranges from 50 to 80 °C degree plays a major role for helping to design stable proteins. How to predict function-unknown proteins to be thermophilic is a long but not fairly resolved problem. Chaos game representation (CGR) can investigate hidden patterns in protein sequences, and also can visually reveal their previously unknown structures. In this paper, using the general form of pseudo amino acid composition to represent protein samples, we proposed a novel method for presenting protein sequence to a CGR picture using CGR algorithm. A 24-dimensional vector extracted from these CGR segments and the first two PCA features are used to classify thermophilic and mesophilic proteins by Support Vector Machine (SVM). Our method is evaluated by the jackknife test. For the 24-dimensional vector, the accuracy is 0.8792 and Matthews Correlation Coefficient (MCC) is 0.7587. The 26-dimensional vector by hybridizing with PCA components performs highly satisfaction, in which the accuracy achieves 0.9944 and MCC achieves 0.9888. The results show the effectiveness of the new hybrid method.

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Section: B Results With Svmsupporting

confidence: 94%

“…The third author has already tested the first group experiment in [66], in which the results are ACC=0.8712 and MCC=0.745. For our updated new data here, for the 24-dimensional vector, by using genetic algorithm, we find C = 578.524063; = 7.822955 be the best one and we get ACC value is 0.8792 and MCC is 0.7587.…”

Section: B Results With Svmmentioning

confidence: 98%

Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Lu¹,

Hu²

2011

PPL

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“…A support vector machine (SVM) is an effective tool for classification and prediction, which has been used in various fields related to protein function prediction, such as prediction of thermal protein (Hu et al, 2009) and soluble protein (Susan, Abhijit, Bhaskar, Valadi, & Petety, 2006). However, it is still a fresh idea for Xoo prediction.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine method on predicting resistance gene against Xanthomonas oryzae pv. oryzae in rice

Xia

Shi

et al. 2010

Expert Systems with Applications

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“…One of the most frequently used feature extraction methods is the so-called FCGR, in which the CGR image is split into small grids and the frequencies of points falling into each grid are taken as the feature of the corresponding protein sequence. For example, in [34][35][36][37][38]41], the CGR image of a protein sequence was split into 24 grids, and the frequencies of points falling into 24 grids are counted and taken as the numerical characteristics of the protein sequence. Under this procedure, a protein sequence can be converted into a 24-dimensional vector.…”

Section: Introductionmentioning

confidence: 99%

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information

Zhang

et al. 2019

BMC Bioinformatics

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Background Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design. Results We introduce DCGR, a novel method for extracting features from protein sequences based on the chaos game representation, which is developed by constructing CGR curves of protein sequences according to physicochemical properties of amino acids, followed by converting the CGR curves into multi-dimensional feature vectors by using the distributions of points in CGR images. Tested on five data sets, DCGR was significantly superior to the state-of-the-art feature extraction methods. Conclusion The DCGR is practically powerful for extracting effective features from protein sequences, and therefore important in similarity analysis of protein sequences, study of protein-protein interactions and prediction of protein functions. It is freely available at https://sourceforge.net/projects/transcriptomeassembly/files/Feature%20Extraction . Electronic supplementary material The online version of this article (10.1186/s12859-019-2943-x) contains supplementary material, which is available to authorized users.

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Chaos Game Representation for Discriminating Thermophilic from Mesophilic Protein Sequences

Cited by 3 publications

References 20 publications

Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Predicting Thermophilic Proteins with Pseudo Amino Acid Composition:Approached from Chaos Game Representation and Principal Component Analysis

Support vector machine method on predicting resistance gene against Xanthomonas oryzae pv. oryzae in rice

DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information

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