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Items: 1 to 20 of 386

1.

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Chen Z, Zhao P, Li F, Marquez-Lago TT, Leier A, Revote J, Zhu Y, Powell DR, Akutsu T, Webb GI, Chou KC, Smith AI, Daly RJ, Li J, Song J.

Brief Bioinform. 2019 Apr 24. pii: bbz041. doi: 10.1093/bib/bbz041. [Epub ahead of print]

PMID:
31067315
2.

Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs.

Chou KC.

Curr Med Chem. 2019 May 6. doi: 10.2174/0929867326666190507082559. [Epub ahead of print]

PMID:
31060481
3.

The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses.

Lu F, Zhu M, Lin Y, Zhong H, Cai L, He L, Chou KC.

J Theor Biol. 2019 Jun 21;471:74-81. doi: 10.1016/j.jtbi.2019.03.017. Epub 2019 Mar 27.

PMID:
30928350
4.

Positive-unlabelled learning of glycosylation sites in the human proteome.

Li F, Zhang Y, Purcell AW, Webb GI, Chou KC, Lithgow T, Li C, Song J.

BMC Bioinformatics. 2019 Mar 6;20(1):112. doi: 10.1186/s12859-019-2700-1.

5.

SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins.

Hussain W, Khan YD, Rasool N, Khan SA, Chou KC.

J Theor Biol. 2019 May 7;468:1-11. doi: 10.1016/j.jtbi.2019.02.007. Epub 2019 Feb 12.

PMID:
30768975
6.

MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.

Zhang M, Li F, Marquez-Lago TT, Leier A, Fan C, Kwoh CK, Chou KC, Song J, Jia C.

Bioinformatics. 2019 Jan 11. doi: 10.1093/bioinformatics/btz016. [Epub ahead of print]

PMID:
30649179
7.

SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.

Hussain W, Khan YD, Rasool N, Khan SA, Chou KC.

Anal Biochem. 2019 Mar 1;568:14-23. doi: 10.1016/j.ab.2018.12.019. Epub 2018 Dec 26.

PMID:
30593778
8.

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. 2018 Dec 17. doi: 10.2174/1573406415666181218102517. [Epub ahead of print]

PMID:
30569871
9.

PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids.

Chandra A, Sharma A, Dehzangi A, Ranganathan S, Jokhan A, Chou KC, Tsunoda T.

Sci Rep. 2018 Dec 18;8(1):17923. doi: 10.1038/s41598-018-36203-8.

10.

pLoc_bal-mVirus: predict subcellular localization of multi-label virus proteins by PseAAC and IHTS treatment to balance training dataset.

Xiao X, Cheng X, Chen G, Mao Q, Chou KC.

Med Chem. 2018 Dec 16. doi: 10.2174/1573406415666181217114710. [Epub ahead of print]

PMID:
30556503
11.

pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments.

Khan YD, Jamil M, Hussain W, Rasool N, Khan SA, Chou KC.

J Theor Biol. 2019 Feb 21;463:47-55. doi: 10.1016/j.jtbi.2018.12.015. Epub 2018 Dec 12.

PMID:
30550863
12.

iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.

Xiao X, Xu ZC, Qiu WR, Wang P, Ge HT, Chou KC.

Genomics. 2018 Dec 5. pii: S0888-7543(18)30613-X. doi: 10.1016/j.ygeno.2018.12.001. [Epub ahead of print]

PMID:
30529532
13.

pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou's General PseAAC.

Ghauri AW, Khan YD, Rasool N, Khan SA, Chou KC.

Curr Pharm Des. 2018;24(34):4034-4043. doi: 10.2174/1381612825666181127101039.

PMID:
30479209
14.

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. doi: 10.2174/1381612824666181119145030.

PMID:
30451108
15.

Simulated Protein Thermal Detection (SPTD) for Enzyme Thermostability Study and an Application Example for Pullulanase from Bacillus deramificans.

Li JX, Wang SQ, Du QS, Wei H, Li XM, Meng JZ, Wang QY, Xie NZ, Huang RB, Chou KC.

Curr Pharm Des. 2018;24(34):4023-4033. doi: 10.2174/1381612824666181113120948.

PMID:
30421671
16.

Bastion3: a two-layer ensemble predictor of type III secreted effectors.

Wang J, Li J, Yang B, Xie R, Marquez-Lago TT, Leier A, Hayashida M, Akutsu T, Zhang Y, Chou KC, Selkrig J, Zhou T, Song J, Lithgow T.

Bioinformatics. 2018 Nov 2. doi: 10.1093/bioinformatics/bty914. [Epub ahead of print]

PMID:
30388198
17.

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

Zhang Y, Xie R, Wang J, Leier A, Marquez-Lago TT, Akutsu T, Webb GI, Chou KC, Song J.

Brief Bioinform. 2018 Aug 24. doi: 10.1093/bib/bby079. [Epub ahead of print]

PMID:
30351377
18.

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 7;460:195-203. doi: 10.1016/j.jtbi.2018.10.021. Epub 2018 Oct 9.

PMID:
30312687
19.

iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Khan YD, Rasool N, Hussain W, Khan SA, Chou KC.

Mol Biol Rep. 2018 Dec;45(6):2501-2509. doi: 10.1007/s11033-018-4417-z. Epub 2018 Oct 11.

PMID:
30311130
20.

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Chen Z, Liu X, Li F, Li C, Marquez-Lago T, Leier A, Akutsu T, Webb GI, Xu D, Smith AI, Li L, Chou KC, Song J.

Brief Bioinform. 2018 Oct 4. doi: 10.1093/bib/bby089. [Epub ahead of print]

PMID:
30285084

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