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Connection

John Reinitz to Enhancer Elements, Genetic

This is a "connection" page, showing publications John Reinitz has written about Enhancer Elements, Genetic.
Connection Strength

2.282
  1. A sequence level model of an intact locus predicts the location and function of nonadditive enhancers. PLoS One. 2017; 12(7):e0180861.
    View in: PubMed
    Score: 0.522
  2. Ancestral resurrection of the Drosophila S2E enhancer reveals accessible evolutionary paths through compensatory change. Mol Biol Evol. 2014 Apr; 31(4):903-16.
    View in: PubMed
    Score: 0.409
  3. A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design. Methods. 2013 Jul 15; 62(1):91-8.
    View in: PubMed
    Score: 0.392
  4. Rearrangements of 2.5 kilobases of noncoding DNA from the Drosophila even-skipped locus define predictive rules of genomic cis-regulatory logic. PLoS Genet. 2013; 9(2):e1003243.
    View in: PubMed
    Score: 0.385
  5. Quantitative and predictive model of transcriptional control of the Drosophila melanogaster even skipped gene. Nat Genet. 2006 Oct; 38(10):1159-65.
    View in: PubMed
    Score: 0.247
  6. An in silico analysis of robust but fragile gene regulation links enhancer length to robustness. PLoS Comput Biol. 2019 11; 15(11):e1007497.
    View in: PubMed
    Score: 0.153
  7. Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation. BMC Syst Biol. 2017 Nov 29; 11(1):116.
    View in: PubMed
    Score: 0.134
  8. The analysis of novel distal Cebpa enhancers and silencers using a transcriptional model reveals the complex regulatory logic of hematopoietic lineage specification. Dev Biol. 2016 May 01; 413(1):128-44.
    View in: PubMed
    Score: 0.030
  9. Transcriptional repression by the Drosophila giant protein: cis element positioning provides an alternative means of interpreting an effector gradient. Development. 1999 Mar; 126(6):1201-10.
    View in: PubMed
    Score: 0.009
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.