ArticleNucleic acids research2007
A new systematic computational approach to predicting target genes of transcription factors.
Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Plant-DTI: Extending the landscape of TF protein and DNA interaction in plants by a machine learning-based approach.Frontiers in plant science · 2022Article
- HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks.Plant & cell physiology · 2016Article
- ORTI: An Open-Access Repository of Transcriptional Interactions for Interrogating Mammalian Gene Expression Data.PloS one · 2016Article
- An improved systematic approach to predicting transcription factor target genes using support vector machine.PloS one · 2014Article
- Bayesian analysis of high-throughput quantitative measurement of protein-DNA interactions.PloS one · 2011Article
- Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster.Bioinformatics (Oxford, England) · 2009Article
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Authors and funding
3 authors.
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Abstract
Identifying transcription factor target genes (TFTGs) is a vital step towards understanding regulatory mechanisms of gene expression. Methods for the de novo identification of TFTGs are generally based on screening for novel DNA binding sites. However, experimental screening of new binding sites is a technically challenging, laborious and time-consuming task, while computational methods still lack accuracy. We propose a novel systematic computational approach for predicting TFTGs directly on a genome scale. Utilizing gene co-expression data, we modeled the prediction problem as a 'yes' or 'no' classification task by converting biological sequences into novel reverse-complementary position-sensitive n-gram profiles and implemented the classifiers with support vector machines. Our approach does not necessarily predict new DNA binding sites, which other studies have shown to be difficult and inaccurate. We applied the proposed approach to predict auxin-response factor target genes from published Arabidopsis thaliana co-expression data and obtained satisfactory results. Using ten-fold cross validations, the area under curve value of the receiver operating characteristic reaches around 0.73.
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