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Items: 1 to 20 of 31

1.

Isoform function prediction based on bi-random walks on a heterogeneous network.

Yu G, Wang K, Domeniconi C, Guo M, Wang J.

Bioinformatics. 2019 Jun 28. pii: btz535. doi: 10.1093/bioinformatics/btz535. [Epub ahead of print]

PMID:
31250882
2.

Weighted matrix factorization on multi-relational data for LncRNA-disease association prediction.

Wang Y, Yu G, Wang J, Fu G, Guo M, Domeniconi C.

Methods. 2019 Jun 18. pii: S1046-2023(19)30038-6. doi: 10.1016/j.ymeth.2019.06.015. [Epub ahead of print]

PMID:
31226302
3.

Predicting protein-protein interactions using high-quality non-interacting pairs.

Zhang L, Yu G, Guo M, Wang J.

BMC Bioinformatics. 2018 Dec 31;19(Suppl 19):525. doi: 10.1186/s12859-018-2525-3.

4.

The study of diffusion kinetics of cinnamaldehyde from corn starch-based film into food simulant and physical properties of antibacterial polymer film.

Ke J, Xiao L, Yu G, Wu H, Shen G, Zhang Z.

Int J Biol Macromol. 2019 Mar 15;125:642-650. doi: 10.1016/j.ijbiomac.2018.12.094. Epub 2018 Dec 10.

PMID:
30543879
5.

ClusterMI: Detecting High-Order SNP Interactions Based on Clustering and Mutual Information.

Cao X, Yu G, Liu J, Jia L, Wang J.

Int J Mol Sci. 2018 Aug 2;19(8). pii: E2267. doi: 10.3390/ijms19082267.

6.

NMFGO: Gene function prediction via nonnegative matrix factorization with Gene Ontology.

Yu G, Wang K, Fu G, Guo M, Wang J.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul 30. doi: 10.1109/TCBB.2018.2861379. [Epub ahead of print]

PMID:
30059316
7.

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction.

Liu J, Yu G, Ren Y, Guo M, Wang J.

Genomics. 2018 Jul 25. pii: S0888-7543(18)30039-9. doi: 10.1016/j.ygeno.2018.07.014. [Epub ahead of print]

PMID:
30055230
8.

BMC3C: binning metagenomic contigs using codon usage, sequence composition and read coverage.

Yu G, Jiang Y, Wang J, Zhang H, Luo H.

Bioinformatics. 2018 Dec 15;34(24):4172-4179. doi: 10.1093/bioinformatics/bty519.

PMID:
29947757
9.

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing.

Zhao Y, Fu G, Wang J, Guo M, Yu G.

Genomics. 2019 May;111(3):334-342. doi: 10.1016/j.ygeno.2018.02.008. Epub 2018 Feb 23.

PMID:
29477548
10.

Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.

Fu G, Wang J, Domeniconi C, Yu G.

Bioinformatics. 2018 May 1;34(9):1529-1537. doi: 10.1093/bioinformatics/btx794.

PMID:
29228285
11.

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.

Wang J, Zhang L, Jia L, Ren Y, Yu G.

Int J Mol Sci. 2017 Nov 8;18(11). pii: E2373. doi: 10.3390/ijms18112373.

12.

HashGO: hashing gene ontology for protein function prediction.

Yu G, Zhao Y, Lu C, Wang J.

Comput Biol Chem. 2017 Dec;71:264-273. doi: 10.1016/j.compbiolchem.2017.09.010. Epub 2017 Oct 4.

PMID:
29031869
13.

BRWLDA: bi-random walks for predicting lncRNA-disease associations.

Yu G, Fu G, Lu C, Ren Y, Wang J.

Oncotarget. 2017 Jul 26;8(36):60429-60446. doi: 10.18632/oncotarget.19588. eCollection 2017 Sep 1.

14.

EnSVMB: Metagenomics Fragments Classification using Ensemble SVM and BLAST.

Jiang Y, Wang J, Xia D, Yu G.

Sci Rep. 2017 Aug 25;7(1):9440. doi: 10.1038/s41598-017-09947-y.

15.

Protein Function Prediction Using Deep Restricted Boltzmann Machines.

Zou X, Wang G, Yu G.

Biomed Res Int. 2017;2017:1729301. doi: 10.1155/2017/1729301. Epub 2017 Jun 28.

16.

NoGOA: predicting noisy GO annotations using evidences and sparse representation.

Yu G, Lu C, Wang J.

BMC Bioinformatics. 2017 Jul 21;18(1):350. doi: 10.1186/s12859-017-1764-z.

17.

NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph.

Yu G, Fu G, Wang J, Zhao Y.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Jul-Aug;15(4):1390-1402. doi: 10.1109/TCBB.2017.2715842. Epub 2017 Jun 15.

PMID:
28641268
18.

HiSeeker: Detecting High-Order SNP Interactions Based on Pairwise SNP Combinations.

Liu J, Yu G, Jiang Y, Wang J.

Genes (Basel). 2017 May 31;8(6). pii: E153. doi: 10.3390/genes8060153.

19.

Network-aided Bi-Clustering for discovering cancer subtypes.

Yu G, Yu X, Wang J.

Sci Rep. 2017 Apr 21;7(1):1046. doi: 10.1038/s41598-017-01064-0.

20.

Clustering cancer gene expression data by projective clustering ensemble.

Yu X, Yu G, Wang J.

PLoS One. 2017 Feb 24;12(2):e0171429. doi: 10.1371/journal.pone.0171429. eCollection 2017.

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