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Items: 1 to 20 of 101

1.

Computational Method for the Identification of Molecular Metabolites Involved in Cereal Hull Color Variations.

Zhang Y, Dong D, Li D, Lu L, Li J, Zhang Y, Chen L.

Comb Chem High Throughput Screen. 2018;21(10):760-770. doi: 10.2174/1386207322666190129105441.

PMID:
30698111
2.

Predicting Citrullination Sites in Protein Sequences Using mRMR Method and Random Forest Algorithm.

Zhang Q, Sun X, Feng K, Wang S, Zhang YH, Wang S, Lu L, Cai YD.

Comb Chem High Throughput Screen. 2017;20(2):164-173. doi: 10.2174/1386207319666161227124350.

PMID:
28029071
3.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

Ma X, Guo J, Sun X.

PLoS One. 2016 Dec 1;11(12):e0167345. doi: 10.1371/journal.pone.0167345. eCollection 2016.

4.

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.

Ma X, Guo J, Sun X.

Biomed Res Int. 2015;2015:425810. doi: 10.1155/2015/425810. Epub 2015 Oct 12.

5.

Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS.

Li BQ, Feng KY, Chen L, Huang T, Cai YD.

PLoS One. 2012;7(8):e43927. doi: 10.1371/journal.pone.0043927. Epub 2012 Aug 28.

6.

Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

Gao YF, Li BQ, Cai YD, Feng KY, Li ZD, Jiang Y.

Mol Biosyst. 2013 Jan 27;9(1):61-9. doi: 10.1039/c2mb25327e. Epub 2012 Nov 2.

PMID:
23117653
7.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

Ni Q, Chen L.

Comb Chem High Throughput Screen. 2017;20(7):612-621. doi: 10.2174/1386207320666170314103147.

PMID:
28292249
8.

Computational method for distinguishing lysine acetylation, sumoylation, and ubiquitination using the random forest algorithm with a feature selection procedure.

Wang S, Li J, Yuan F, Huang T, Cai YD.

Comb Chem High Throughput Screen. 2017 Dec 17. doi: 10.2174/1386207321666171218114056. [Epub ahead of print]

PMID:
29256343
9.

Prediction of Nitrated Tyrosine Residues in Protein Sequences by Extreme Learning Machine and Feature Selection Methods.

Chen L, Wang S, Zhang YH, Wei L, Xu X, Huang T, Cai YD.

Comb Chem High Throughput Screen. 2018;21(6):393-402. doi: 10.2174/1386207321666180531091619.

PMID:
29848272
10.

Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.

Li F, Zhao C, Xia Z, Wang Y, Zhou X, Li GZ.

BMC Complement Altern Med. 2012 Aug 16;12:127. doi: 10.1186/1472-6882-12-127.

11.

Predicting A-to-I RNA editing by feature selection and random forest.

Shu Y, Zhang N, Kong X, Huang T, Cai YD.

PLoS One. 2014 Oct 22;9(10):e110607. doi: 10.1371/journal.pone.0110607. eCollection 2014.

12.

Prediction of interactiveness of proteins and nucleic acids based on feature selections.

Yuan Y, Shi X, Li X, Lu W, Cai Y, Gu L, Liu L, Li M, Kong X, Xing M.

Mol Divers. 2010 Nov;14(4):627-33. doi: 10.1007/s11030-009-9198-9. Epub 2009 Oct 9.

PMID:
19816781
13.

Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.

Wolahan SM, Hirt D, Glenn TC.

In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25.

14.

A novel method of predicting protein disordered regions based on sequence features.

Zhao TH, Jiang M, Huang T, Li BQ, Zhang N, Li HP, Cai YD.

Biomed Res Int. 2013;2013:414327. doi: 10.1155/2013/414327. Epub 2013 Apr 22.

15.

Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.

Liu L, Chen L, Zhang YH, Wei L, Cheng S, Kong X, Zheng M, Huang T, Cai YD.

J Biomol Struct Dyn. 2017 Feb;35(2):312-329. doi: 10.1080/07391102.2016.1138142. Epub 2016 Apr 4.

PMID:
26750516
16.

Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

Wang S, Zhang YH, Huang G, Chen L, Cai YD.

Comb Chem High Throughput Screen. 2017;20(2):96-106. doi: 10.2174/1386207319666161220114424.

PMID:
28000567
17.

Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection.

Wang S, Zhang YH, Zhang N, Chen L, Huang T, Cai YD.

Comb Chem High Throughput Screen. 2017;20(7):582-593. doi: 10.2174/1386207320666170310115754.

PMID:
28294058
18.

Computational method for identifying malonylation sites by using random forest algorithm.

Wang S, Li J, Sun X, Zhang YH, Huang T, Cai Y.

Comb Chem High Throughput Screen. 2018 Dec 27. doi: 10.2174/1386207322666181227144318. [Epub ahead of print]

PMID:
30588879
19.

Prediction of protein domain with mRMR feature selection and analysis.

Li BQ, Hu LL, Chen L, Feng KY, Cai YD, Chou KC.

PLoS One. 2012;7(6):e39308. doi: 10.1371/journal.pone.0039308. Epub 2012 Jun 15.

20.

Predicting FAD Interacting Residues with Feature Selection and Comprehensive Sequence Descriptors.

Yang R, Zhang C, Gao R, Zhang L, Song Q.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Apr 9. doi: 10.1109/TCBB.2018.2824332. [Epub ahead of print]

PMID:
29993986

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