193 related articles for article (PubMed ID: 23043905)
1. Mining SOM expression portraits: feature selection and integrating concepts of molecular function.
Wirth H; von Bergen M; Binder H
BioData Min; 2012 Oct; 5(1):18. PubMed ID: 23043905
[TBL] [Abstract][Full Text] [Related]
2. Expression cartography of human tissues using self organizing maps.
Wirth H; Löffler M; von Bergen M; Binder H
BMC Bioinformatics; 2011 Jul; 12():306. PubMed ID: 21794127
[TBL] [Abstract][Full Text] [Related]
3. Improving cluster visualization in self-organizing maps: application in gene expression data analysis.
Fernandez EA; Balzarini M
Comput Biol Med; 2007 Dec; 37(12):1677-89. PubMed ID: 17544390
[TBL] [Abstract][Full Text] [Related]
4. A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data.
Ray SS; Ganivada A; Pal SK
IEEE Trans Neural Netw Learn Syst; 2016 Sep; 27(9):1890-906. PubMed ID: 26285222
[TBL] [Abstract][Full Text] [Related]
5. Clustering of gene expression data: performance and similarity analysis.
Yin L; Huang CH; Ni J
BMC Bioinformatics; 2006 Dec; 7 Suppl 4(Suppl 4):S19. PubMed ID: 17217511
[TBL] [Abstract][Full Text] [Related]
6. MALDI-typing of infectious algae of the genus Prototheca using SOM portraits.
Wirth H; von Bergen M; Murugaiyan J; Rösler U; Stokowy T; Binder H
J Microbiol Methods; 2012 Jan; 88(1):83-97. PubMed ID: 22062088
[TBL] [Abstract][Full Text] [Related]
7. The novel hierarchical clustering approach using self-organizing map with optimum dimension selection.
Tripathi K
Health Care Sci; 2024 Apr; 3(2):88-100. PubMed ID: 38939618
[TBL] [Abstract][Full Text] [Related]
8. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.
Schmidt M; Avagyan S; Reiche K; Binder H; Loeffler-Wirth H
Curr Issues Mol Biol; 2024 May; 46(5):4701-4720. PubMed ID: 38785552
[TBL] [Abstract][Full Text] [Related]
9. Analysis and visualization of gene expression data using self-organizing maps.
Nikkilä J; Törönen P; Kaski S; Venna J; Castrén E; Wong G
Neural Netw; 2002; 15(8-9):953-66. PubMed ID: 12416686
[TBL] [Abstract][Full Text] [Related]
10. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study.
Wang J; Delabie J; Aasheim H; Smeland E; Myklebost O
BMC Bioinformatics; 2002 Nov; 3():36. PubMed ID: 12445336
[TBL] [Abstract][Full Text] [Related]
11. Gene expression analysis in clear cell renal cell carcinoma using gene set enrichment analysis for biostatistical management.
Maruschke M; Reuter D; Koczan D; Hakenberg OW; Thiesen HJ
BJU Int; 2011 Jul; 108(2 Pt 2):E29-35. PubMed ID: 21435154
[TBL] [Abstract][Full Text] [Related]
12. Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps.
Brameier M; Wiuf C
J Biomed Inform; 2007 Apr; 40(2):160-73. PubMed ID: 16824804
[TBL] [Abstract][Full Text] [Related]
13. Application of Multi-SOM clustering approach to macrophage gene expression analysis.
Ghouila A; Yahia SB; Malouche D; Jmel H; Laouini D; Guerfali FZ; Abdelhak S
Infect Genet Evol; 2009 May; 9(3):328-36. PubMed ID: 18992849
[TBL] [Abstract][Full Text] [Related]
14. Exploiting data topology in visualization and clustering of self-organizing maps.
Taşdemir K; Merényi E
IEEE Trans Neural Netw; 2009 Apr; 20(4):549-62. PubMed ID: 19228556
[TBL] [Abstract][Full Text] [Related]
15. Methodology and software to detect viral integration site hot-spots.
Presson AP; Kim N; Xiaofei Y; Chen IS; Kim S
BMC Bioinformatics; 2011 Sep; 12():367. PubMed ID: 21914224
[TBL] [Abstract][Full Text] [Related]
16. LSOR: Longitudinally-Consistent Self-Organized Representation Learning.
Ouyang J; Zhao Q; Adeli E; Peng W; Zaharchuk G; Pohl KM
Med Image Comput Comput Assist Interv; 2023 Oct; 14220():279-289. PubMed ID: 37961067
[TBL] [Abstract][Full Text] [Related]
17. Estimating the number of clusters in multivariate data by self-organizing maps.
Costa JA; Netto ML
Int J Neural Syst; 1999 Jun; 9(3):195-202. PubMed ID: 10560758
[TBL] [Abstract][Full Text] [Related]
18. An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.
Hsu AL; Tang SL; Halgamuge SK
Bioinformatics; 2003 Nov; 19(16):2131-40. PubMed ID: 14594719
[TBL] [Abstract][Full Text] [Related]
19. Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification.
Phan P; Mezghani N; Wai EK; de Guise J; Labelle H
Spine J; 2013 Nov; 13(11):1527-33. PubMed ID: 24095098
[TBL] [Abstract][Full Text] [Related]
20. SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma.
Liu E; Zhang ZZ; Cheng X; Liu X; Cheng L
BMC Med Genomics; 2020 Apr; 13(Suppl 5):50. PubMed ID: 32241274
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
