ArticleOncotarget2016
iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 149 citations in OpenAlex.
- GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.Frontiers in pharmacology · 2022Review
- Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources.Clinical proteomics · 2020Review
- Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.IEEE access : practical innovations, open solutions · 2020Article
- iN6-methylat (5-step): identifying DNA NMolecular genetics and genomics : MGG · 2019Article
- iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAACCurrent genomics · 2019Article
- iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical MomentsCurrent genomics · 2019Article
- Identifying Acetylation Protein by Fusing Its PseAAC and Functional Domain Annotation.Frontiers in bioengineering and biotechnology · 2019Article
- RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.Database : the journal of biological databases and curation · 2019Article
- LAIPT: Lysine Acetylation Site Identification with Polynomial Tree.International journal of molecular sciences · 2018Article
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- A Novel Modeling in Mathematical Biology for Classification of Signal Peptides.Scientific reports · 2018Article
- Article
- 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.Oncotarget · 2017Article
- iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.Oncotarget · 2017Article
- Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.Molecules (Basel, Switzerland) · 2017Article
- Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.Oncotarget · 2017Article
- Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.Scientific reports · 2017Article
- iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.Oncotarget · 2017Article
- iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.Molecular therapy. Nucleic acids · 2017Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein phosphorylation is a posttranslational modification (PTM or PTLM), where a phosphoryl group is added to the residue(s) of a protein molecule. The most commonly phosphorylated amino acids occur at serine (S), threonine (T), and tyrosine (Y). Protein phosphorylation plays a significant role in a wide range of cellular processes; meanwhile its dysregulation is also involved with many diseases. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of S, T, or Y, which ones can be phosphorylated, and which ones cannot? To address this problem, we have developed a predictor called iPhos-PseEn by fusing four different pseudo component approaches (amino acids' disorder scores, nearest neighbor scores, occurrence frequencies, and position weights) into an ensemble classifier via a voting system. Rigorous cross-validations indicated that the proposed predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iPhos-PseEn has been established at http://www.jci-bioinfo.cn/iPhos-PseEn, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.
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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.