ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

Background Uncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN a...

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Veröffentlicht in:	BMC plant biology Jg. 14; H. 1; S. 97
Hauptverfasser:	Chávez Montes, Ricardo A, Coello, Gerardo, González-Aguilera, Karla L, Marsch-Martínez, Nayelli, de Folter, Stefan, Alvarez-Buylla, Elena R
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 16.04.2014 BioMed Central Ltd Springer Nature B.V
Schlagworte:	Agriculture Algorithms Analysis animal development Arabidopsis - genetics Arabidopsis - radiation effects Arabidopsis Proteins - genetics Arabidopsis Proteins - metabolism Arabidopsis thaliana Bioinformatics Biomedical and Life Sciences Cell Wall - metabolism Cell Wall - radiation effects data collection Data Mining Databases, Genetic Development and cell biology DNA binding proteins Epistasis, Genetic - radiation effects Gene Expression Profiling Gene Expression Regulation, Plant - radiation effects Gene Regulatory Networks - genetics Genes Genetic aspects Genomics High-Throughput Nucleotide Sequencing Life Sciences microarray technology Mutation - genetics Oligonucleotide Array Sequence Analysis Physiological aspects Plant Roots - genetics Plant Roots - radiation effects Plant Sciences prediction Research Article rooting roots Time Factors tissues transcription (genetics) transcription factors Transcription Factors - genetics Transcription Factors - metabolism Tree Biology Ultraviolet Rays Root Transcription factor Arabidopsis Transcriptional regulatory networks ARACNe
ISSN:	1471-2229, 1471-2229
Online-Zugang:	Volltext
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