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Erschienen in:
Accuracy Assessment of Consensus Sequence from Shotgun Sequencing Kapitel lesen Erstes Kapitel lesen

2011 | OriginalPaper | Buchkapitel

3. Statistical Learning and Modeling of TF-DNA Binding

verfasst von : Bo Jiang, Jun S. Liu

Erschienen in: Handbook of Statistical Bioinformatics

Verlag: Springer Berlin Heidelberg

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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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Vorheriges Kapitel Statistical and Computational Studies on Alternative Splicing
Nächstes Kapitel Computational Promoter Prediction in a Vertebrate Genome
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Metadaten
Titel
Statistical Learning and Modeling of TF-DNA Binding
verfasst von
Bo Jiang
Jun S. Liu
Copyright-Jahr
2011
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Statistical Bioinformatics

Print ISBN: 978-3-642-16344-9

Electronic ISBN: 978-3-642-16345-6

Copyright-Jahr: 2011

DOI
https://doi.org/10.1007/978-3-642-16345-6_3