312 related articles for article (PubMed ID: 27323404)
1. iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.
Qiu WR; Xiao X; Xu ZC; Chou KC
Oncotarget; 2016 Aug; 7(32):51270-51283. PubMed ID: 27323404
[TBL] [Abstract][Full Text] [Related]
2. iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.
Qiu WR; Sun BQ; Xiao X; Xu D; Chou KC
Mol Inform; 2017 May; 36(5-6):. PubMed ID: 28488814
[TBL] [Abstract][Full Text] [Related]
3. iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
Oncotarget; 2016 Jun; 7(23):34558-70. PubMed ID: 27153555
[TBL] [Abstract][Full Text] [Related]
4. pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
J Theor Biol; 2016 Apr; 394():223-230. PubMed ID: 26807806
[TBL] [Abstract][Full Text] [Related]
5. iPTM-mLys: identifying multiple lysine PTM sites and their different types.
Qiu WR; Sun BQ; Xiao X; Xu ZC; Chou KC
Bioinformatics; 2016 Oct; 32(20):3116-3123. PubMed ID: 27334473
[TBL] [Abstract][Full Text] [Related]
6. iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
Anal Biochem; 2016 Mar; 497():48-56. PubMed ID: 26723495
[TBL] [Abstract][Full Text] [Related]
7. iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.
Qiu WR; Sun BQ; Xiao X; Xu ZC; Chou KC
Oncotarget; 2016 Jul; 7(28):44310-44321. PubMed ID: 27322424
[TBL] [Abstract][Full Text] [Related]
8. iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier.
Qiu WR; Sun BQ; Xiao X; Xu ZC; Jia JH; Chou KC
Genomics; 2018 Sep; 110(5):239-246. PubMed ID: 29107015
[TBL] [Abstract][Full Text] [Related]
9. iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC.
Liu LM; Xu Y; Chou KC
Med Chem; 2017; 13(6):552-559. PubMed ID: 28521678
[TBL] [Abstract][Full Text] [Related]
10. Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.
Qiu WR; Zheng QS; Sun BQ; Xiao X
Mol Inform; 2017 Mar; 36(3):. PubMed ID: 27681207
[TBL] [Abstract][Full Text] [Related]
11. iRNA-2methyl: Identify RNA 2'-O-methylation Sites by Incorporating Sequence-Coupled Effects into General PseKNC and Ensemble Classifier.
Qiu WR; Jiang SY; Sun BQ; Xiao X; Cheng X; Chou KC
Med Chem; 2017; 13(8):734-743. PubMed ID: 28641529
[TBL] [Abstract][Full Text] [Related]
12. iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
J Theor Biol; 2015 Jul; 377():47-56. PubMed ID: 25908206
[TBL] [Abstract][Full Text] [Related]
13. iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
Molecules; 2016 Jan; 21(1):E95. PubMed ID: 26797600
[TBL] [Abstract][Full Text] [Related]
14. Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition.
Jia J; Liu Z; Xiao X; Liu B; Chou KC
J Biomol Struct Dyn; 2016 Sep; 34(9):1946-61. PubMed ID: 26375780
[TBL] [Abstract][Full Text] [Related]
15. iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model.
Qiu WR; Xiao X; Lin WZ; Chou KC
J Biomol Struct Dyn; 2015; 33(8):1731-42. PubMed ID: 25248923
[TBL] [Abstract][Full Text] [Related]
16. iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.
Qiu WR; Jiang SY; Xu ZC; Xiao X; Chou KC
Oncotarget; 2017 Jun; 8(25):41178-41188. PubMed ID: 28476023
[TBL] [Abstract][Full Text] [Related]
17. pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.
Jia J; Zhang L; Liu Z; Xiao X; Chou KC
Bioinformatics; 2016 Oct; 32(20):3133-3141. PubMed ID: 27354696
[TBL] [Abstract][Full Text] [Related]
18. iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines.
Hasan MAM; Ahmad S; Molla MKI
Mol Biosyst; 2017 Jul; 13(8):1608-1618. PubMed ID: 28682387
[TBL] [Abstract][Full Text] [Related]
19. iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC.
Jia J; Li X; Qiu W; Xiao X; Chou KC
J Theor Biol; 2019 Jan; 460():195-203. PubMed ID: 30312687
[TBL] [Abstract][Full Text] [Related]
20. pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties.
Liu Z; Xiao X; Yu DJ; Jia J; Qiu WR; Chou KC
Anal Biochem; 2016 Mar; 497():60-7. PubMed ID: 26748145
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]