579 related articles for article (PubMed ID: 17473321)
21. Optimal number of features as a function of sample size for various classification rules.
Hua J; Xiong Z; Lowey J; Suh E; Dougherty ER
Bioinformatics; 2005 Apr; 21(8):1509-15. PubMed ID: 15572470
[TBL] [Abstract][Full Text] [Related]
22. Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.
Martinez E; Alvarez MM; Trevino V
Comput Biol Chem; 2010 Aug; 34(4):244-50. PubMed ID: 20888301
[TBL] [Abstract][Full Text] [Related]
23. Incremental forward feature selection with application to microarray gene expression data.
Lee YJ; Chang CC; Chao CH
J Biopharm Stat; 2008; 18(5):827-40. PubMed ID: 18781519
[TBL] [Abstract][Full Text] [Related]
24. Improving protein protein interaction prediction based on phylogenetic information using a least-squares support vector machine.
Craig RA; Liao L
Ann N Y Acad Sci; 2007 Dec; 1115():154-67. PubMed ID: 17925357
[TBL] [Abstract][Full Text] [Related]
25. A review of feature selection techniques in bioinformatics.
Saeys Y; Inza I; Larrañaga P
Bioinformatics; 2007 Oct; 23(19):2507-17. PubMed ID: 17720704
[TBL] [Abstract][Full Text] [Related]
26. Tumor classification ranking from microarray data.
Hewett R; Kijsanayothin P
BMC Genomics; 2008 Sep; 9 Suppl 2(Suppl 2):S21. PubMed ID: 18831787
[TBL] [Abstract][Full Text] [Related]
27. A parsimonious threshold-independent protein feature selection method through the area under receiver operating characteristic curve.
Wang Z; Chang YC; Ying Z; Zhu L; Yang Y
Bioinformatics; 2007 Oct; 23(20):2788-94. PubMed ID: 17878205
[TBL] [Abstract][Full Text] [Related]
28. Learning regulatory programs that accurately predict differential expression with MEDUSA.
Kundaje A; Lianoglou S; Li X; Quigley D; Arias M; Wiggins CH; Zhang L; Leslie C
Ann N Y Acad Sci; 2007 Dec; 1115():178-202. PubMed ID: 17934055
[TBL] [Abstract][Full Text] [Related]
29. Bias in error estimation when using cross-validation for model selection.
Varma S; Simon R
BMC Bioinformatics; 2006 Feb; 7():91. PubMed ID: 16504092
[TBL] [Abstract][Full Text] [Related]
30. Comparative evaluation of set-level techniques in predictive classification of gene expression samples.
Holec M; Kléma J; Zelezný F; Tolar J
BMC Bioinformatics; 2012 Jun; 13 Suppl 10(Suppl 10):S15. PubMed ID: 22759420
[TBL] [Abstract][Full Text] [Related]
31. Evaluation of clustering algorithms for gene expression data.
Datta S; Datta S
BMC Bioinformatics; 2006 Dec; 7 Suppl 4(Suppl 4):S17. PubMed ID: 17217509
[TBL] [Abstract][Full Text] [Related]
32. Superior feature-set ranking for small samples using bolstered error estimation.
Sima C; Braga-Neto U; Dougherty ER
Bioinformatics; 2005 Apr; 21(7):1046-54. PubMed ID: 15514003
[TBL] [Abstract][Full Text] [Related]
33. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.
Zhang X; Lu X; Shi Q; Xu XQ; Leung HC; Harris LN; Iglehart JD; Miron A; Liu JS; Wong WH
BMC Bioinformatics; 2006 Apr; 7():197. PubMed ID: 16606446
[TBL] [Abstract][Full Text] [Related]
34. Genetic test bed for feature selection.
Choudhary A; Brun M; Hua J; Lowey J; Suh E; Dougherty ER
Bioinformatics; 2006 Apr; 22(7):837-42. PubMed ID: 16428263
[TBL] [Abstract][Full Text] [Related]
35. Monte Carlo feature selection for supervised classification.
Draminski M; Rada-Iglesias A; Enroth S; Wadelius C; Koronacki J; Komorowski J
Bioinformatics; 2008 Jan; 24(1):110-7. PubMed ID: 18048398
[TBL] [Abstract][Full Text] [Related]
36. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.
De Martino F; Valente G; Staeren N; Ashburner J; Goebel R; Formisano E
Neuroimage; 2008 Oct; 43(1):44-58. PubMed ID: 18672070
[TBL] [Abstract][Full Text] [Related]
37. Growing Bayesian network models of gene networks from seed genes.
Peña JM; Björkegren J; Tegnér J
Bioinformatics; 2005 Sep; 21 Suppl 2():ii224-9. PubMed ID: 16204109
[TBL] [Abstract][Full Text] [Related]
38. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.
Chen Z; Li J; Wei L
Artif Intell Med; 2007 Oct; 41(2):161-75. PubMed ID: 17851055
[TBL] [Abstract][Full Text] [Related]
39. Fast and Scalable Feature Selection for Gene Expression Data Using Hilbert-Schmidt Independence Criterion.
Gangeh MJ; Zarkoob H; Ghodsi A
IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(1):167-181. PubMed ID: 28182548
[TBL] [Abstract][Full Text] [Related]
40. A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.
Demichelis F; Magni P; Piergiorgi P; Rubin MA; Bellazzi R
BMC Bioinformatics; 2006 Nov; 7():514. PubMed ID: 17125514
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
