A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides

Xu, Lei; Liang, Guangmin; Wang, Longjie; Liao, Changrui

doi:10.3390/genes9030158

Cited by 97 publications

(70 citation statements)

References 57 publications

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“…The sequence information of proteins should be described precisely in the process of protein representation [ 10 , 11 ]. The study of protein representation has been paid more attention these years, such as the amino acid composition (AAC) model used in [ 12 , 13 , 14 ], g-gap dipeptide composition, proposed in [ 15 ], 400D [ 16 ], 188D [ 17 ], and others. The protein is represented by a simple vector in AAC model, whose elements represent the normalized occurrence frequency of the native amino acid in the peptide chain.…”

Section: Introductionmentioning

confidence: 99%

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Liang

Shi

et al. 2018

IJMS

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Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.

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Section: Introductionmentioning

confidence: 99%

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Liang

Shi

et al. 2018

IJMS

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“…SVM is a widely used machine learning algorithm (Ding and Li, 2015;Li et al, 2015;Zeng et al, 2017;Ding et al, 2017a;Zhang et al, 2019;Tan et al, 2019a) and was used in this study to identify 6mA sites in the rice genome. SVM is also widely used in bioinformatics fields (Zou et al, 2016b;Wang et al, 2018;Wei et al, 2018;Xiong et al, 2018;Zeng et al, 2018a;Xu et al, 2018a;Xu et al, 2018b;Xu et al, 2018c;Li et al, 2019). Our experiments showed that SVM was more suitable for the purposes of the present study than were the other algorithms.…”

Section: Support Vector Machinementioning

confidence: 73%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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“…Feature selection is an effective way to remove redundant information and prevent over-fitting in machine learning modeling (Tang et al, 2017;Xu et al, 2018a;Cheng et al, 2019a;Liu, 2019;Sun et al, 2019;. Several feature selection technologies, including ANOVA (Lv et al, 2019b) and MRMD (Zou et al, 2016), have been developed and are widely used for DNA, RNA, and protein identification (Xu et al, 2018b). In this work, an LGBM (Ke et al, 2017) 1 wrapper was used to select appropriate feature spaces for model training.…”

Section: Feature Selectionmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides

Cited by 97 publications

References 57 publications

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

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