These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
24. iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique. Mahmood MK; Ehsan A; Khan YD; Chou KC Curr Genomics; 2020 Nov; 21(7):536-545. PubMed ID: 33214770 [TBL] [Abstract][Full Text] [Related]
25. pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset. Xiao X; Cheng X; Chen G; Mao Q; Chou KC Med Chem; 2019; 15(5):496-509. PubMed ID: 30556503 [TBL] [Abstract][Full Text] [Related]
26. iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Xiao X; Min JL; Wang P; Chou KC J Theor Biol; 2013 Nov; 337():71-9. PubMed ID: 23988798 [TBL] [Abstract][Full Text] [Related]
27. iPreny-PseAAC: Identify C-terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC. Xu Y; Wang Z; Li C; Chou KC Med Chem; 2017; 13(6):544-551. PubMed ID: 28425870 [TBL] [Abstract][Full Text] [Related]
28. 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]
29. iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. Xu Y; Shao XJ; Wu LY; Deng NY; Chou KC PeerJ; 2013; 1():e171. PubMed ID: 24109555 [TBL] [Abstract][Full Text] [Related]
30. pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset. Chou KC; Cheng X; Xiao X Med Chem; 2019; 15(5):472-485. PubMed ID: 30569871 [TBL] [Abstract][Full Text] [Related]
31. pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset. Cheng X; Xiao X; Chou KC Curr Pharm Des; 2018; 24(34):4013-4022. PubMed ID: 30451108 [TBL] [Abstract][Full Text] [Related]
32. iSuc-PseAAC: predicting lysine succinylation in proteins by incorporating peptide position-specific propensity. Xu Y; Ding YX; Ding J; Lei YH; Wu LY; Deng NY Sci Rep; 2015 Jun; 5():10184. PubMed ID: 26084794 [TBL] [Abstract][Full Text] [Related]
33. pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Cheng X; Xiao X; Chou KC Bioinformatics; 2018 May; 34(9):1448-1456. PubMed ID: 29106451 [TBL] [Abstract][Full Text] [Related]
34. pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC. Cheng X; Xiao X; Chou KC Mol Biosyst; 2017 Aug; 13(9):1722-1727. PubMed ID: 28702580 [TBL] [Abstract][Full Text] [Related]
35. iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approach. Qiu WR; Xiao X; Lin WZ; Chou KC Biomed Res Int; 2014; 2014():947416. PubMed ID: 24977164 [TBL] [Abstract][Full Text] [Related]
36. Phogly-PseAAC: Prediction of lysine phosphoglycerylation in proteins incorporating with position-specific propensity. Xu Y; Ding YX; Ding J; Wu LY; Deng NY J Theor Biol; 2015 Aug; 379():10-5. PubMed ID: 25913879 [TBL] [Abstract][Full Text] [Related]
37. iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. Xiao X; Min JL; Wang P; Chou KC PLoS One; 2013; 8(8):e72234. PubMed ID: 24015221 [TBL] [Abstract][Full Text] [Related]
38. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Cheng X; Xiao X; Chou KC Genomics; 2018 Jan; 110(1):50-58. PubMed ID: 28818512 [TBL] [Abstract][Full Text] [Related]
39. iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition. Liu B; Xu J; Lan X; Xu R; Zhou J; Wang X; Chou KC PLoS One; 2014; 9(9):e106691. PubMed ID: 25184541 [TBL] [Abstract][Full Text] [Related]
40. Predicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system model. Lin WZ; Fang JA; Xiao X; Chou KC PLoS One; 2012; 7(11):e49040. PubMed ID: 23189138 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]