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Connection

Marc Libault to Gene Expression Profiling

This is a "connection" page, showing publications Marc Libault has written about Gene Expression Profiling.
Connection Strength

0.981
  1. An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. Plant J. 2010 Jul 01; 63(1):86-99.
    View in: PubMed
    Score: 0.292
  2. Complete transcriptome of the soybean root hair cell, a single-cell model, and its alteration in response to Bradyrhizobium japonicum infection. Plant Physiol. 2010 Feb; 152(2):541-52.
    View in: PubMed
    Score: 0.285
  3. A comparative genomic and transcriptomic analysis at the level of isolated root hair cells reveals new conserved root hair regulatory elements. Plant Mol Biol. 2017 Aug; 94(6):641-655.
    View in: PubMed
    Score: 0.121
  4. Large-scale analysis of putative soybean regulatory gene expression identifies a Myb gene involved in soybean nodule development. Plant Physiol. 2009 Nov; 151(3):1207-20.
    View in: PubMed
    Score: 0.070
  5. Transcription profiling of soybean nodulation by Bradyrhizobium japonicum. Mol Plant Microbe Interact. 2008 May; 21(5):631-45.
    View in: PubMed
    Score: 0.064
  6. Identification of 118 Arabidopsis transcription factor and 30 ubiquitin-ligase genes responding to chitin, a plant-defense elicitor. Mol Plant Microbe Interact. 2007 Aug; 20(8):900-11.
    View in: PubMed
    Score: 0.061
  7. Isolating and cryopreserving pig skin cells for single-cell RNA sequencing study. PLoS One. 2022; 17(2):e0263869.
    View in: PubMed
    Score: 0.042
  8. Identification and functional characterization of soybean root hair microRNAs expressed in response to Bradyrhizobium japonicum infection. Plant Biotechnol J. 2016 Jan; 14(1):332-41.
    View in: PubMed
    Score: 0.026
  9. Identification of quantitative trait loci controlling gene expression during the innate immunity response of soybean. Plant Physiol. 2011 Dec; 157(4):1975-86.
    View in: PubMed
    Score: 0.020
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.

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