Reconstructing transcription factor activities in hierarchical transcription network motifs

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چکیده

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Reconstructing transcription factor activities in hierarchical transcription network motifs

MOTIVATION A knowledge of the dynamics of transcription factors is fundamental to understand the transcriptional regulation mechanism. Nowadays, an experimental measure of transcription factor activities in vivo represents a challenge. Several methods have been developed to infer these activities from easily measurable quantities such as mRNA expression of target genes. A limitation of these me...

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Motivation: Generating small models for which there may exist very little training data presents a crucial problem in computational biology, namely the trade-oo between model speciicity and under-tting the data. There are a bevy of superior modeling techniques; however, certain domain speciic problems, such as modeling the regulatory regions in intergenic DNA impose constraints on the modeling ...

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Using Temporal Correlation in Factor Analysis for Reconstructing Transcription Factor Activities

Two-level gene regulatory networks consist of the transcription factors (TFs) in the top level and their regulated genes in the second level. The expression profiles of the regulated genes are the observed high-throughput data given by experiments such as microarrays. The activity profiles of the TFs are treated as hidden variables as well as the connectivity matrix that indicates the regulator...

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Bayesian Clustering of Transcription Factor Binding Motifs

Genes are often regulated in living cells by proteins called transcription factors (TFs) that bind directly to short segments of DNA in close proximity to specific genes. These binding sites have a conserved nucleotide appearance, which is called a motif. Several recent studies of transcriptional regulation require the reduction of a large collection of motifs into clusters based on the similar...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2011

ISSN: 1460-2059,1367-4803

DOI: 10.1093/bioinformatics/btr487