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Items: 1 to 20 of 53

1.

A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data.

Li Y, Kang K, Krahn JM, Croutwater N, Lee K, Umbach DM, Li L.

BMC Genomics. 2017 Jul 3;18(1):508. doi: 10.1186/s12864-017-3906-0.

2.

Markov Boundary Discovery with Ridge Regularized Linear Models.

Strobl EV, Visweswaran S.

J Causal Inference. 2016 Mar;4(1):31-48. Epub 2015 Nov 3.

3.

Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.

Wang S, Liu S.

Int J Mol Sci. 2015 Dec 19;16(12):30343-61. doi: 10.3390/ijms161226237.

4.
5.

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms.

Li S, Li L, Zeng Q, Zhang Y, Guo Z, Liu Z, Jin M, Su C, Lin L, Xu J, Liu S.

Sci Rep. 2015 May 7;5:9582. doi: 10.1038/srep09582.

6.

Toward predicting metastatic progression of melanoma based on gene expression data.

Li Y, Krahn JM, Flake GP, Umbach DM, Li L.

Pigment Cell Melanoma Res. 2015 Jul;28(4):453-63. doi: 10.1111/pcmr.12374. Epub 2015 Apr 24.

7.

Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm.

Saberkari H, Shamsi M, Joroughi M, Golabi F, Sedaaghi MH.

J Med Signals Sens. 2014 Oct;4(4):291-8.

8.

A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information.

Han F, Sun W, Ling QH.

PLoS One. 2014 May 20;9(5):e97530. doi: 10.1371/journal.pone.0097530. eCollection 2014.

9.

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.

Li S, Kang L, Zhao XM.

Biomed Res Int. 2014;2014:362738. doi: 10.1155/2014/362738. Epub 2014 Mar 6. Review.

11.

Cuckoo search epistasis: a new method for exploring significant genetic interactions.

Aflakparast M, Salimi H, Gerami A, Dubé MP, Visweswaran S, Masoudi-Nejad A.

Heredity (Edinb). 2014 Jun;112(6):666-74. doi: 10.1038/hdy.2014.4. Epub 2014 Feb 19.

12.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

Yang M, Li X, Li Z, Ou Z, Liu M, Liu S, Li X, Yang S.

PLoS One. 2013 Dec 30;8(12):e84253. doi: 10.1371/journal.pone.0084253. eCollection 2013.

13.

Analyzing kernel matrices for the identification of differentially expressed genes.

Xia XL, Xing H, Liu X.

PLoS One. 2013 Dec 9;8(12):e81683. doi: 10.1371/journal.pone.0081683. eCollection 2013.

14.

Fusing Gene Interaction to Improve Disease Discrimination on Classification Analysis.

Zhang JG, Li J, Tang W, Deng HW.

Adv Genet Eng. 2012 Feb 9;1(1):1000102.

15.
16.

Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio).

Wang RL, Bencic D, Biales A, Flick R, Lazorchak J, Villeneuve D, Ankley GT.

BMC Genomics. 2012 Aug 1;13:358. doi: 10.1186/1471-2164-13-358.

17.

Pattern-driven neighborhood search for biclustering of microarray data.

Ayadi W, Elloumi M, Hao JK.

BMC Bioinformatics. 2012 May 8;13 Suppl 7:S11. doi: 10.1186/1471-2105-13-S7-S11.

18.

SPICE: discovery of phenotype-determining component interplays.

Chen Z, Padmanabhan K, Rocha AM, Shpanskaya Y, Mihelcic JR, Scott K, Samatova NF.

BMC Syst Biol. 2012 May 14;6:40. doi: 10.1186/1752-0509-6-40.

19.

Analysis of biological features associated with meiotic recombination hot and cold spots in Saccharomyces cerevisiae.

Hansen L, Kim NK, Mariño-Ramírez L, Landsman D.

PLoS One. 2011;6(12):e29711. doi: 10.1371/journal.pone.0029711. Epub 2011 Dec 29.

20.

Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis.

Wang Z, Palade V.

BMC Genomics. 2011;12 Suppl 2:S5. doi: 10.1186/1471-2164-12-S2-S5. Epub 2011 Jul 27.

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