Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.1186/1752-0509-7-S6-S16

http://scihub22266oqcxt.onion/10.1186/1752-0509-7-S6-S16
suck pdf from google scholar
C4029456!4029456 !24565409
unlimited free pdf from europmc24565409
    free
PDF from PMC    free
html from PMC    free

Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24565409 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215

suck abstract from ncbi

pmid24565409
      BMC+Syst+Biol 2013 ; 7 Suppl 6 (Suppl 6 ): S16
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data #MMPMID24565409
  • Podsiad?o A ; Wrzesie? M ; Paja W ; Rudnicki W ; Wilczy?ski B
  • BMC Syst Biol 2013[]; 7 Suppl 6 (Suppl 6 ): S16 PMID24565409 show ga
  • BACKGROUND: Transcriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately. RESULTS: In this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs. CONCLUSIONS: Based on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set of enhancers can generalize with significant accuracy beyond the training set.
  • |*Nucleotide Motifs [MESH]
  • |*Sequence Analysis [MESH]
  • |Animals [MESH]
  • |Chromatin Immunoprecipitation [MESH]
  • |Chromatin/*genetics [MESH]
  • |Computational Biology/*methods [MESH]
  • |Drosophila melanogaster/genetics [MESH]
  • |Enhancer Elements, Genetic/*genetics [MESH]
  • |Epigenesis, Genetic [MESH]
  • |Genetic Markers/genetics [MESH]
  • |Histones/genetics [MESH]


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    Linkout box