iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Liu, Bin; Chen, Shengyu; Yan, Ke; Weng, Fan

doi:10.3389/fgene.2019.00842

Cited by 31 publications

(16 citation statements)

References 46 publications

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“…As a powerful and popular method, the SVM has been extensively used in bioinformatics [83][84][85][86][87][88][89][90][91][92][93]. In this study, an SVM was implemented using the scikit-learn toolkit, which is available at https://scikit-learn.org/.…”

Section: F Support Vector Machinementioning

confidence: 99%

Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Wang

Ding

et al. 2020

IEEE Access

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Recently, several machine-learning-based DNA N-4-methycytosine (4mC) predictors have been developed to provide deeper insight into the biological functions and mechanisms of 4mC. However, the performance of the existing classifiers for identification of Escherichia coli DNA 4mC sites is inadequate. Here, we present a new support vector machine 4mC predictor, named iEC4mC-SVM, for Escherichia coli (E.coli) DNA 4mC site identification, optimized using light gradient boosting machine feature selection technology. The iEC4mC-SVM predictor had a 10-fold cross-validation accuracy of 85.4% and Jackknife crossvalidation accuracy of 84.9%. The 83.2% independent testing accuracy of iEC4mC-SVM was 1.0-6.5% higher than those of state-of-the-art E. coli DNA 4mC site predictors. A t-distributed stochastic neighbor embedding analysis confirmed that the prediction performance enhancement of iEC4mC-SVM was due to the light gradient boosting machine feature selection. INDEX TERMS Bioinformatics, DNA, machine learning, support vector machine, sequences.

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Section: F Support Vector Machinementioning

confidence: 99%

Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Wang

Ding

et al. 2020

IEEE Access

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“…The peptide is composed of 20 sorts of amino acids (Liu et al, 2019a). The frequency of every amino acid type in a peptide sequence was computed to present the peptide sequences.…”

Section: Amino Acid Compositionmentioning

confidence: 99%

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Zhou

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.

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“…Random forest (Liaw and Wiener, 2002) is an integrated algorithm that integrates multiple trees through the idea of integrated learning. It has been widely used in bioinformatics (Liu et al, 2019a;Manavalan et al, 2019b,c;Wang et al, 2019;Lv Z. B. et al, 2020;Wang M. et al, 2020).…”

Section: Random Forestmentioning

confidence: 99%

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

Zhai

Chen

Teng

et al. 2020

Front. Cell Dev. Biol.

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Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body's oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model's sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.

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iRO-PsekGCC: Identify DNA Replication Origins Based on Pseudo k-Tuple GC Composition

Cited by 31 publications

References 46 publications

Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Escherichia Coli DNA N-4-Methycytosine Site Prediction Accuracy Improved by Light Gradient Boosting Machine Feature Selection Technology

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

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