ArticleScientific reports2016
In silico identification of enhancers on the basis of a combination of transcription factor binding motif occurrences.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Discovering the complete enhancer map of human herpesviruses using a natural language processing model.Nature communications · 2025Article
- Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.BMC biology · 2024Article
- Epigenomic landscape of enhancer elements during Hydra head organizer formation.Epigenetics & chromatin · 2020Article
- iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.BMC genomics · 2019Article
- DNA Methylation of Enhancer Elements in Myeloid Neoplasms: Think Outside the Promoters?Cancers · 2019Review
- Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI.International journal of computer assisted radiology and surgery · 2017Article
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Authors and funding
5 authors.
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Abstract
Enhancers interact with gene promoters and form chromatin looping structures that serve important functions in various biological processes, such as the regulation of gene transcription and cell differentiation. However, enhancers are difficult to identify because they generally do not have fixed positions or consensus sequence features, and biological experiments for enhancer identification are costly in terms of labor and expense. In this work, several models were built by using various sequence-based feature sets and their combinations for enhancer prediction. The selected features derived from a recursive feature elimination method showed that the model using a combination of 141 transcription factor binding motif occurrences from 1,422 transcription factor position weight matrices achieved a favorably high prediction accuracy superior to that of other reported methods. The models demonstrated good prediction accuracy for different enhancer datasets obtained from different cell lines/tissues. In addition, prediction accuracy was further improved by integration of chromatin state features. Our method is complementary to wet-lab experimental methods and provides an additional method to identify enhancers.
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