ArticlePeerJ2017
Prediction of post-translational modification sites using multiple kernel support vector machine.
Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.Briefings in bioinformatics · 2025Article
- PreMLS: The undersampling technique based on ClusterCentroids to predict multiple lysine sites.PLoS computational biology · 2024Article
- Comprehensive computational analysis reveals YXXΦ[I/L/M/F/V] motif and YXXΦ-like tetrapeptides across HFRS causing Hantaviruses and their association with viral pathogenesis and host immune regulation.Frontiers in immunology · 2022Article
- Article
- Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer.BMC bioinformatics · 2020Article
- Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix.BMC molecular and cell biology · 2019Article
- High Throughput Chemical Screening Reveals Multiple Regulatory Proteins on FOXA1 in Breast Cancer Cell Lines.International journal of molecular sciences · 2018Article
- PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.Scientific reports · 2018Article
- Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.Genomics, proteomics & bioinformatics · 2018Article
- PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile.International journal of biological sciences · 2018Article
- ksrMKL: a novel method for identification of kinase-substrate relationships using multiple kernel learning.PeerJ · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.