Abstract
Discovering binding sites and motifs of specific TFs is an important first step towards the understanding of gene regulation circuitry. Computational approaches have been developed to identify transcription factor binding sites from a set of co-regulated genes. Recently, the abundance of gene expression data, ChIP-based TF-binding data (ChIP-array/seq), and high-resolution epigenetic maps have brought up the possibility of capturing sequence features relevant to TF-DNA interactions so as to improve the predictive power of gene regulation modeling. In this chapter, we introduce some statistical models and computational strategies used to predict TF-DNA interactions from the DNA sequence information, and describe a general framework of predictive modeling approaches to the TF-DNA binding problem, which includes both traditional regression methods and statistical learning methods by selecting relevant sequence features and epigenetic markers.
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Jiang, B., Liu, J.S. (2011). Statistical Learning and Modeling of TF-DNA Binding. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_3
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DOI: https://doi.org/10.1007/978-3-642-16345-6_3
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