ArticleNucleic acids research2009
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data.
Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.PLoS computational biology · 2021Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- 3Scover: Identifying Safeguard TF from Cell Type-TF Specificity Network by an Extended Minimum Set Cover Model.iScience · 2020Article
- Development of a computational promoter with highly efficient expression in tumors.BMC cancer · 2018Article
- Article
- A Network of Splice Isoforms for the Mouse.Scientific reports · 2016Article
- Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets.PloS one · 2016Article
- YCRD: Yeast Combinatorial Regulation Database.PloS one · 2016Article
- CoopTFD: a repository for predicted yeast cooperative transcription factor pairs.Database : the journal of biological databases and curation · 2016Article
- Predicting the functions of long noncoding RNAs using RNA-seq based on Bayesian network.BioMed research international · 2015Article
- PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.BMC bioinformatics · 2015Article
- Properly defining the targets of a transcription factor significantly improves the computational identification of cooperative transcription factor pairs in yeast.BMC genomics · 2015Article
- Combinatorial epigenetic patterns as quantitative predictors of chromatin biology.BMC genomics · 2014Article
- Identifying cooperative transcription factors in yeast using multiple data sources.BMC systems biology · 2014Article
- A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.BMC systems biology · 2014Article
- Epigenetic coordination of signaling pathways during the epithelial-mesenchymal transition.Epigenetics & chromatin · 2013Article
- One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data.Nucleic acids research · 2012Article
- A novel unbiased measure for motif co-occurrence predicts combinatorial regulation of transcription.BMC genomics · 2012Article
- Structural constraints in collaborative competition of transcription factors against the nucleosome.Journal of molecular biology · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.