154 related articles for article (PubMed ID: 29990003)
1. Switched Latent Force Models for Reverse-Engineering Transcriptional Regulation in Gene Expression Data.
Lopez-Lopera AF; Alvarez MA
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):322-335. PubMed ID: 29990003
[TBL] [Abstract][Full Text] [Related]
2. Characterizing regulatory path motifs in integrated networks using perturbational data.
Joshi A; Van Parys T; Van de Peer Y; Michoel T
Genome Biol; 2010; 11(3):R32. PubMed ID: 20230615
[TBL] [Abstract][Full Text] [Related]
3. SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data.
Wang H; Lian Y; Li C; Ma Y; Yan Z; Dong C
J Bioinform Comput Biol; 2019 Dec; 17(6):1950035. PubMed ID: 32019417
[TBL] [Abstract][Full Text] [Related]
4. Mathematical Programming for Modeling Expression of a Gene Using Gurobi Optimizer to Identify Its Transcriptional Regulators.
Muley VY
Methods Mol Biol; 2021; 2328():99-113. PubMed ID: 34251621
[TBL] [Abstract][Full Text] [Related]
5. Transcriptional network inference from functional similarity and expression data: a global supervised approach.
Ambroise J; Robert A; Macq B; Gala JL
Stat Appl Genet Mol Biol; 2012 Jan; 11(1):Article 2. PubMed ID: 22499684
[TBL] [Abstract][Full Text] [Related]
6. Simultaneous inference and clustering of transcriptional dynamics in gene regulatory networks.
Asif HM; Sanguinetti G
Stat Appl Genet Mol Biol; 2013 Oct; 12(5):545-57. PubMed ID: 24051920
[TBL] [Abstract][Full Text] [Related]
7. Switching regulatory models of cellular stress response.
Sanguinetti G; Ruttor A; Opper M; Archambeau C
Bioinformatics; 2009 May; 25(10):1280-6. PubMed ID: 19279066
[TBL] [Abstract][Full Text] [Related]
8. CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.
Roy S; Sheikh SZ; Furey TS
PLoS Comput Biol; 2024 Apr; 20(4):e1012016. PubMed ID: 38630807
[TBL] [Abstract][Full Text] [Related]
9. Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks.
Awad S; Panchy N; Ng SK; Chen J
J Bioinform Comput Biol; 2012 Oct; 10(5):1250012. PubMed ID: 22849367
[TBL] [Abstract][Full Text] [Related]
10. TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.
Gunasekara C; Zhang K; Deng W; Brown L; Wei H
Nucleic Acids Res; 2018 Jun; 46(11):e67. PubMed ID: 29579312
[TBL] [Abstract][Full Text] [Related]
11. Interpreting patterns of gene expression: signatures of coregulation, the data processing inequality, and triplet motifs.
Ku WL; Duggal G; Li Y; Girvan M; Ott E
PLoS One; 2012; 7(2):e31969. PubMed ID: 22393375
[TBL] [Abstract][Full Text] [Related]
12. Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory.
Sayyed-Ahmad A; Tuncay K; Ortoleva PJ
BMC Bioinformatics; 2007 Jan; 8():20. PubMed ID: 17244365
[TBL] [Abstract][Full Text] [Related]
13. Refining current knowledge on the yeast FLR1 regulatory network by combined experimental and computational approaches.
Teixeira MC; Dias PJ; Monteiro PT; Sala A; Oliveira AL; Freitas AT; Sá-Correia I
Mol Biosyst; 2010 Dec; 6(12):2471-81. PubMed ID: 20938527
[TBL] [Abstract][Full Text] [Related]
14. Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
Marbach D; Schaffter T; Mattiussi C; Floreano D
J Comput Biol; 2009 Feb; 16(2):229-39. PubMed ID: 19183003
[TBL] [Abstract][Full Text] [Related]
15. Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization.
Hasegawa T; Yamaguchi R; Nagasaki M; Miyano S; Imoto S
PLoS One; 2014; 9(8):e105942. PubMed ID: 25162401
[TBL] [Abstract][Full Text] [Related]
16. Systems biology approach identifies key regulators and the interplay between miRNAs and transcription factors for pathological cardiac hypertrophy.
Recamonde-Mendoza M; Werhli AV; Biolo A
Gene; 2019 May; 698():157-169. PubMed ID: 30844478
[TBL] [Abstract][Full Text] [Related]
17. Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.
Li H; Zhan M
Bioinformatics; 2008 Sep; 24(17):1874-80. PubMed ID: 18586698
[TBL] [Abstract][Full Text] [Related]
18. Large-scale learning of combinatorial transcriptional dynamics from gene expression.
Asif HM; Sanguinetti G
Bioinformatics; 2011 May; 27(9):1277-83. PubMed ID: 21367870
[TBL] [Abstract][Full Text] [Related]
19. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.
Kentzoglanakis K; Poole M
IEEE/ACM Trans Comput Biol Bioinform; 2012; 9(2):358-71. PubMed ID: 21576756
[TBL] [Abstract][Full Text] [Related]
20. Global analysis of gene transcription regulation in prokaryotes.
Zhou D; Yang R
Cell Mol Life Sci; 2006 Oct; 63(19-20):2260-90. PubMed ID: 16927028
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]