Prediction of antioxidant proteins using hybrid feature representation method and random forest

Ao, Chunyan; Zhou, Wenyang; Gao, Lin; Dong, Benzhi; Yu, Liang

doi:10.1016/j.ygeno.2020.08.016

Cited by 57 publications

(18 citation statements)

References 82 publications

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“…The proposed method was also compared with the work of Ao et al., [22] and the results are shown in Table 1. In order to make the comparison fair, we used the raw, unequalized data of Dataset 1 for the experiment.…”

Section: Resultsmentioning

confidence: 99%

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ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

et al. 2021

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Antioxidant proteins can terminate a chain of reactions caused by free radicals and protect cells from damage. To identify antioxidant proteins rapidly, a computational model was proposed based on the optimized recoding scheme, sequence information and machine learning methods. First, over 600 recoding schemes were collected to build a scheme set. Then, the original sequence was recoded as a reduced expression whose g‐gap dipeptides (g = 0, 1, 2) were used as the features of proteins. Furthermore, a random forest method was used to evaluate the classification ability of the obtained dipeptide features. After going through all schemes, the best predictive performance scheme was chosen as the optimized reduction scheme. Finally, for the RF method, a grid search strategy was used to select a better parameter combination to identify antioxidant proteins. In the experiment, the present method correctly recognized 90.13–99.87% of the antioxidant samples. Other experimental results also proved that the present method was efficient to identify antioxidant proteins. Finally, we also developed a web server that was freely accessible to researchers.

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Section: Resultsmentioning

confidence: 99%

“…Ao et al. [22] adopted four types of features to generate hybrid features and used three feature selection methods to optimize the feature set. The RF method was used as the classifier, and it predicted more than 95.5% of the antioxidant proteins.…”

Section: Introductionmentioning

confidence: 99%

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

et al. 2021

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“…188D extracts sequence features based on 20 amino acid compositions and eight physicochemical properties ( Ao et al, 2020 ). These features encode the primary sequence with 188-dimensional vectors ( Li et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Liu

Chen

Zhang

et al. 2021

Front. Cell Dev. Biol.

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Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.

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“…A feature selection method for binary classification problems was introduced by Dashtban, Balafar & Suravajhala (2018) , in which the traditional bat algorithm is extended with more refined formulations, improved and multi-objective operators and a novel local search strategy. Other examples of feature selection methods could be found in MotieGhader et al (2020) , Dashtban & Balafar (2017) , Nematzadeh et al (2019) , Maghsoudloo et al (2020) , Rostami et al (2020) , Shamsara & Shamsara (2020) , Ao et al (2020) , Statnikov et al (2005) , Rana et al (2019) , Chamikara et al (2016) , Nardone, Ciaramella & Staiano (2019) and the references cited therein.…”

Section: Related Workmentioning

confidence: 99%

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

Hamraz

Gul

Raza

et al. 2021

PeerJ Computer Science

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In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

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Prediction of antioxidant proteins using hybrid feature representation method and random forest

Cited by 57 publications

References 82 publications

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

ORS‐Pred: An optimized reduced scheme‐based identifier for antioxidant proteins

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

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