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Items: 1 to 20 of 25

1.

Feature Subset Selection for Cancer Classification Using Weight Local Modularity.

Zhao G, Wu Y.

Sci Rep. 2016 Oct 5;6:34759. doi: 10.1038/srep34759.

2.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.

Xi M, Sun J, Liu L, Fan F, Wu X.

Comput Math Methods Med. 2016;2016:3572705. doi: 10.1155/2016/3572705. Epub 2016 Aug 24.

3.

Gene selection for cancer classification with the help of bees.

Moosa JM, Shakur R, Kaykobad M, Rahman MS.

BMC Med Genomics. 2016 Aug 10;9 Suppl 2:47. doi: 10.1186/s12920-016-0204-7.

4.

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.

5.

Pathway and network approaches for identification of cancer signature markers from omics data.

Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW.

J Cancer. 2015 Jan 1;6(1):54-65. doi: 10.7150/jca.10631. eCollection 2015. Review.

6.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Weidlich IE, Pevzner Y, Miller BT, Filippov IV, Woodcock HL, Brooks BR.

J Comput Chem. 2015 Jan 5;36(1):62-7. doi: 10.1002/jcc.23765. Epub 2014 Nov 3.

7.

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.

8.

Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm.

Chen KH, Wang KJ, Tsai ML, Wang KM, Adrian AM, Cheng WC, Yang TS, Teng NC, Tan KP, Chang KS.

BMC Bioinformatics. 2014 Feb 20;15:49. doi: 10.1186/1471-2105-15-49.

9.

A robust hybrid approach based on estimation of distribution algorithm and support vector machine for hunting candidate disease genes.

Li L, Chen H, Liu C, Wang F, Zhang F, Bai L, Chen Y, Peng L.

ScientificWorldJournal. 2013;2013:393570. doi: 10.1155/2013/393570. Epub 2013 Feb 7.

10.

Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification.

Wang SL, Li XL, Fang J.

BMC Bioinformatics. 2012 Jul 25;13:178. doi: 10.1186/1471-2105-13-178.

11.

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.

12.

Lee Pedersen's work in theoretical and computational chemistry and biochemistry.

Pedersen LG.

World J Biol Chem. 2011 Feb 26;2(2):35-8. doi: 10.4331/wjbc.v2.i2.35.

13.

Class prediction for high-dimensional class-imbalanced data.

Blagus R, Lusa L.

BMC Bioinformatics. 2010 Oct 20;11:523. doi: 10.1186/1471-2105-11-523.

14.

Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification.

Hou ML, Wang SL, Li XL, Lei YK.

J Biomed Biotechnol. 2010;2010:726413. doi: 10.1155/2010/726413. Epub 2010 Jun 23.

16.

Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.

Liu T, Lin N, Shi N, Zhang B.

BMC Bioinformatics. 2009 May 15;10:146. doi: 10.1186/1471-2105-10-146.

17.

DFP: a Bioconductor package for fuzzy profile identification and gene reduction of microarray data.

Glez-Peña D, Alvarez R, Díaz F, Fdez-Riverola F.

BMC Bioinformatics. 2009 Jan 29;10:37. doi: 10.1186/1471-2105-10-37.

18.

Data mining in genomics.

Lee JK, Williams PD, Cheon S.

Clin Lab Med. 2008 Mar;28(1):145-66, viii. doi: 10.1016/j.cll.2007.10.010.

19.

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.

Gormley M, Dampier W, Ertel A, Karacali B, Tozeren A.

BMC Bioinformatics. 2007 Oct 26;8:415.

20.

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