Predicting Enzyme Subclasses by Using Random Forest with Multicharacteristic Parameters

Wang, Ying; Hu, Xiuzhen; Sun, Lixia; Feng, Zhenxing; Song, Hangyu

doi:10.2174/09298665113206660114

Cited by 5 publications

(3 citation statements)

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“…Considering the local conservation of fold sequences, the sequence of each protein fold was divided into n segments, and in each segment, the occurrence frequencies of 20 amino acid residues in the protein sequences were extracted as a parameter, as previously described (Chen and Li, 2007, Wang et al, 2014). Thus, the initial parameter of each sequence was converted into a 20*n-dimensional vector that was inputted into the ID algorithm for classification, and an improved result was obtained.…”

Section: Methodsmentioning

confidence: 99%

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The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

Feng

Jiang

et al. 2016

Saudi Journal of Biological Sciences

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The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.

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Section: Methodsmentioning

confidence: 99%

“…As feature parameters, motif information has been successfully applied for the prediction of superfamilies, protein folds, etc. (Ben-Hur and Brutlag, 2003, Liu et al, 2012, Wang et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

Feng

Jiang

et al. 2016

Saudi Journal of Biological Sciences

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“…For example, some motifs are related to DNA-binding sites and enzyme catalytic sites [29]. As feature parameters, motif information has been successfully applied for the prediction of super family, protein folds, and so forth [27, 28, 30]. Two kinds of motifs were used in this paper: one with biological functions obtained by searching the existing functional motif dataset PROSITE [31] and the other with statistically significant motifs searched by MEME (http://meme.nbcr.net/meme/cgi-bin/meme.cgi).…”

Section: Methodsmentioning

confidence: 99%

Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information

Feng

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. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

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Experiment to Find Out Suitable Machine Learning Algorithm for Enzyme Subclass Classification

Saran,

Ghosh,

Das

et al. 2024

Lecture Notes in Networks and Systems

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Predicting Enzyme Subclasses by Using Random Forest with Multicharacteristic Parameters

Cited by 5 publications

References 0 publications

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information

Experiment to Find Out Suitable Machine Learning Algorithm for Enzyme Subclass Classification

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