Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models

Author(s):  
Claudia Rangel ◽  
John Angus ◽  
Zoubin Ghahramani ◽  
David L. Wild
2004 ◽  
Vol 20 (9) ◽  
pp. 1361-1372 ◽  
Author(s):  
C. Rangel ◽  
J. Angus ◽  
Z. Ghahramani ◽  
M. Lioumi ◽  
E. Sotheran ◽  
...  

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.


Epigenomics ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 1347-1366 ◽  
Author(s):  
Maria Abbondanza Pantaleo ◽  
Gloria Ravegnini ◽  
Annalisa Astolfi ◽  
Vittorio Simeon ◽  
Margherita Nannini ◽  
...  

2016 ◽  
Vol 12 (10) ◽  
pp. e1005146 ◽  
Author(s):  
Daifeng Wang ◽  
Fei He ◽  
Sergei Maslov ◽  
Mark Gerstein

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201071 ◽  
Author(s):  
Chayanin Tangsuwansri ◽  
Thanit Saeliw ◽  
Surangrat Thongkorn ◽  
Weerasak Chonchaiya ◽  
Kanya Suphapeetiporn ◽  
...  

Author(s):  
Fumio Arai ◽  
Kentaro Hosokawa ◽  
Yoshiko Matsumoto ◽  
Hirofumi Toyama ◽  
Toshio Suda

2002 ◽  
Vol 69 ◽  
pp. 135-142 ◽  
Author(s):  
Elena M. Comelli ◽  
Margarida Amado ◽  
Steven R. Head ◽  
James C. Paulson

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip® arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.


2007 ◽  
Vol 177 (4S) ◽  
pp. 93-93
Author(s):  
Toshiyuki Tsunoda ◽  
Junichi Inocuchi ◽  
Darren Tyson ◽  
Seiji Naito ◽  
David K. Ornstein

2004 ◽  
Vol 171 (4S) ◽  
pp. 198-199 ◽  
Author(s):  
Ximing J. Yang ◽  
Jun Sugimura ◽  
Maria S. Tretiakova ◽  
Bin T. Teh

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