Integrating Diverse Datasets Improves Developmental Enhancer Prediction

@inproceedings{Erwin2014IntegratingDD,
  title={Integrating Diverse Datasets Improves Developmental Enhancer Prediction},
  author={Genevieve D. Erwin and Nir Oksenberg and Rebecca M. Truty and Dennis Kostka and Karl K. Murphy and Nadav Ahituv and Katherine S. Pollard and John A. Capra},
  booktitle={PLoS Computational Biology},
  year={2014}
}
Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary… CONTINUE READING
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