179 related articles for article (PubMed ID: 23815553)
1. A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes.
Pio G; Ceci M; D'Elia D; Loglisci C; Malerba D
BMC Bioinformatics; 2013; 14 Suppl 7(Suppl 7):S8. PubMed ID: 23815553
[TBL] [Abstract][Full Text] [Related]
2. Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach.
Pio G; Malerba D; D'Elia D; Ceci M
BMC Bioinformatics; 2014; 15 Suppl 1(Suppl 1):S4. PubMed ID: 24564296
[TBL] [Abstract][Full Text] [Related]
3. ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks.
Pio G; Ceci M; Malerba D; D'Elia D
BMC Bioinformatics; 2015; 16 Suppl 9(Suppl 9):S7. PubMed ID: 26051695
[TBL] [Abstract][Full Text] [Related]
4. Discovery of error-tolerant biclusters from noisy gene expression data.
Gupta R; Rao N; Kumar V
BMC Bioinformatics; 2011 Nov; 12 Suppl 12(Suppl 12):S1. PubMed ID: 22168285
[TBL] [Abstract][Full Text] [Related]
5. COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification.
Maind A; Raut S
J Biosci; 2019 Jun; 44(2):. PubMed ID: 31180061
[TBL] [Abstract][Full Text] [Related]
6. A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions.
Su L; Liu G; Wang J; Xu D
Methods; 2019 Aug; 166():22-30. PubMed ID: 31121299
[TBL] [Abstract][Full Text] [Related]
7. An evaluation study of biclusters visualization techniques of gene expression data.
Aouabed H; Elloumi M; Santamaría R
J Integr Bioinform; 2021 Oct; 18(4):. PubMed ID: 34699698
[No Abstract] [Full Text] [Related]
8. Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms.
Chia BK; Karuturi RK
Algorithms Mol Biol; 2010 May; 5():23. PubMed ID: 20507637
[TBL] [Abstract][Full Text] [Related]
9. Discovery and visualization of miRNA-mRNA functional modules within integrated data using bicluster analysis.
Bryan K; Terrile M; Bray IM; Domingo-Fernandéz R; Watters KM; Koster J; Versteeg R; Stallings RL
Nucleic Acids Res; 2014 Feb; 42(3):e17. PubMed ID: 24357407
[TBL] [Abstract][Full Text] [Related]
10. Identification of miRNA-mRNA regulatory modules by exploring collective group relationships.
Masud Karim SM; Liu L; Le TD; Li J
BMC Genomics; 2016 Jan; 17 Suppl 1(Suppl 1):7. PubMed ID: 26817421
[TBL] [Abstract][Full Text] [Related]
11. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle.
Oliveira GB; Regitano LCA; Cesar ASM; Reecy JM; Degaki KY; Poleti MD; Felício AM; Koltes JE; Coutinho LL
BMC Genomics; 2018 Feb; 19(1):126. PubMed ID: 29415651
[TBL] [Abstract][Full Text] [Related]
12. Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.
Yoon S; Nguyen HCT; Jo W; Kim J; Chi SM; Park J; Kim SY; Nam D
Nucleic Acids Res; 2019 May; 47(9):e53. PubMed ID: 30820547
[TBL] [Abstract][Full Text] [Related]
13. Correlation of expression profiles between microRNAs and mRNA targets using NCI-60 data.
Wang YP; Li KB
BMC Genomics; 2009 May; 10():218. PubMed ID: 19435500
[TBL] [Abstract][Full Text] [Related]
14. Cancer-specific functional profiling in microsatellite-unstable (MSI) colon and endometrial cancers using combined differentially expressed genes and biclustering analysis.
Na W; Lee IJ; Koh I; Kwon M; Song YS; Lee SH
Medicine (Baltimore); 2023 May; 102(19):e33647. PubMed ID: 37171359
[TBL] [Abstract][Full Text] [Related]
15. Gene bi-targeting by viral and human miRNAs.
Veksler-Lublinsky I; Shemer-Avni Y; Kedem K; Ziv-Ukelson M
BMC Bioinformatics; 2010 May; 11():249. PubMed ID: 20465802
[TBL] [Abstract][Full Text] [Related]
16. Identification of microRNA-mRNA modules using microarray data.
Jayaswal V; Lutherborrow M; Ma DD; Yang YH
BMC Genomics; 2011 Mar; 12():138. PubMed ID: 21375780
[TBL] [Abstract][Full Text] [Related]
17. Integrated analysis of the miRNA-mRNA next-generation sequencing data for finding their associations in different cancer types.
Bhowmick SS; Bhattacharjee D; Rato L
Comput Biol Chem; 2020 Feb; 84():107152. PubMed ID: 31785969
[TBL] [Abstract][Full Text] [Related]
18. CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.
Xiao Q; Luo J; Liang C; Cai J; Li G; Cao B
BMC Bioinformatics; 2019 Feb; 20(1):67. PubMed ID: 30732558
[TBL] [Abstract][Full Text] [Related]
19. Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data.
Jayaswal V; Lutherborrow M; Ma DD; Hwa Yang Y
Nucleic Acids Res; 2009 May; 37(8):e60. PubMed ID: 19295134
[TBL] [Abstract][Full Text] [Related]
20. Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization.
Cheng KO; Law NF; Siu WC; Liew AW
BMC Bioinformatics; 2008 Apr; 9():210. PubMed ID: 18433478
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]