Sequence clustering in bioinformatics: an empirical study

Zou, Quan; Lin, Gang; Jiang, Xingpeng; Liu, Xiangrong; Zeng, Xiangxiang

doi:10.1093/bib/bby090

Cited by 128 publications

(96 citation statements)

References 58 publications

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“…Additionally, we used 10-fold cross validation method and jackknife test to evaluate the predictive performance ( Wei et al, 2017a ; Zeng et al, 2017a , b ; Liao et al, 2018 ; Zou et al, 2018 ). The two evaluation methods were chosen since existing methods in the literature used them for performance evaluation.…”

Section: Methodsmentioning

confidence: 99%

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

Qiang

Chen

et al. 2018

Front. Genet.

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As one of the well-studied RNA methylation modifications, N6-methyladenosine (m6A) plays important roles in various biological progresses, such as RNA splicing and degradation, etc. Identification of m6A sites is fundamentally important for better understanding of their functional mechanisms. Recently, machine learning based prediction methods have emerged as an effective approach for fast and accurate identification of m6A sites. In this paper, we proposed “M6AMRFS”, a new machine learning based predictor for the identification of m6A sites. In this predictor, we exploited a new feature representation algorithm to encode RNA sequences with two feature descriptors (dinucleotide binary encoding and Local position-specific dinucleotide frequency), and used the F-score algorithm combined with SFS (Sequential Forward Search) to enhance the feature representation ability. To predict m6A sites, we employed the eXtreme Gradient Boosting (XGBoost) algorithm to build a predictive model. Benchmarking results showed that the proposed predictor is competitive with the state-of-the art predictors. Importantly, robust predictions for multiple species by our predictor demonstrate that our predictive models have strong generalization ability. To the best of our knowledge, M6AMRFS is the first tool that can be used for the identification of m6A sites in multiple species. To facilitate the use of our predictor, we have established a user-friendly webserver with the implementation of M6AMRFS, which is currently available in http://server.malab.cn/M6AMRFS/. We anticipate that it will be a useful tool for the relevant research of m6A sites.

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Section: Methodsmentioning

confidence: 99%

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

Qiang

Chen

et al. 2018

Front. Genet.

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“…This hyperplane can maximize the margin between the two classes, and support vectors define the hyperplane. SVM has been applied to many tasks in bioinformatics (Wei et al, 2014(Wei et al, , 2016(Wei et al, , 2018Ding et al, 2017;He et al, 2018;Zou et al, 2018;Fang et al, 2019;Zeng et al, 2019b,c;Zhang M. et al, 2019;Zhang X. et al, 2019;Zhu et al, 2019).…”

Section: Support Vector Machinementioning

confidence: 99%

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

Hou

Wang

2020

Front. Genet.

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ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer cells. Therefore, the identification of ABC transporters is an urgent task. The present study used 188D as the feature extraction method, which is based on sequence information and physicochemical properties. We also visualized the feature extracted by t-Distributed Stochastic Neighbor Embedding (t-SNE). The sample based on the features extracted by 188D may be separated. Further, random forest (RF) is an efficient classifier to identify proteins. Under the 10-fold cross-validation of the model proposed here for a training set, the average accuracy rate of 10 training sets was 89.54%. We obtained values of 0.87 for specificity, 0.92 for sensitivity, and 0.79 for MCC. In the testing set, the accuracy achieved was 89%. These results suggest that the model combining 188D with RF is an optimal tool to identify ABC transporters.

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“…Second, we removed the proteins that contained unknown residues or less than 50 residues because unknown residues may confuse the prediction model, and sequences of less than 50 residues tend to be peptides rather than complete protein sequences. Next, to reduce the negative influence of data redundancy and homology bias [17], we removed the homologous proteins with >30% similarity. CD-HIT [18] was used to cluster the remaining proteins with a sequence identity cut-off of 0.3, and the representative protein of each cluster was selected.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Computational Identification and Analysis of Ubiquinone-Binding Proteins

Lü

Jiang

Wang

et al. 2020

Cells

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Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In this work, we were the first to propose a UBPs predictor (UBPs-Pred). The optimal feature subset selected from three categories of sequence-derived features was fed into the extreme gradient boosting (XGBoost) classifier, and the parameters of XGBoost were tuned by multi-objective particle swarm optimization (MOPSO). The experimental results over the independent validation demonstrated considerable prediction performance with a Matthews correlation coefficient (MCC) of 0.517. After that, we analyzed the UBPs using bioinformatics methods, including the statistics of the binding domain motifs and protein distribution, as well as an enrichment analysis of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway.

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Sequence clustering in bioinformatics: an empirical study

Cited by 128 publications

References 58 publications

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

Predicting ATP-Binding Cassette Transporters Using the Random Forest Method

Computational Identification and Analysis of Ubiquinone-Binding Proteins

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