ArticlePLoS computational biology2008
A predictive model of the oxygen and heme regulatory network in yeast.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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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.
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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.
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Who cites it
30 citing papers in PubMed, 43 citations in OpenAlex.
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- Heme promotes transcriptional and demethylase activities of Gis1, a member of the histone demethylase JMJD2/KDM4 family.Nucleic acids research · 2018Article
- Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Rigid geometry solves "curse of dimensionality" effects in clustering methods: An application to omics data.PloS one · 2017Article
- The Swi3 protein plays a unique role in regulating respiration in eukaryotes.Bioscience reports · 2016Article
- Molecular mechanisms of system responses to novel stimuli are predictable from public data.Nucleic acids research · 2014Article
- Master regulators, regulatory networks, and pathways of glioblastoma subtypes.Cancer informatics · 2014Article
- PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data.Bioinformatics (Oxford, England) · 2013Article
- Mapping yeast transcriptional networks.Genetics · 2013Review
- Article
- The nuclear localization of SWI/SNF proteins is subjected to oxygen regulation.Cell & bioscience · 2012Article
- Integrating heterogeneous gene expression data for gene regulatory network modelling.Theory in biosciences = Theorie in den Biowissenschaften · 2012Article
- Post-translational modification of Cu/Zn superoxide dismutase under anaerobic conditions.Biochemistry · 2012Article
- Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.Molecular systems biology · 2012Article
- Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.PloS one · 2012Article
- Genomic analysis of immune response against Vibrio cholerae hemolysin in Caenorhabditis elegans.PloS one · 2012Article
- Hypoxia elicits broad and systematic changes in protein subcellular localization.American journal of physiology. Cell physiology · 2011Article
- Deletion of a subgroup of ribosome-related genes minimizes hypoxia-induced changes and confers hypoxia tolerance.Physiological genomics · 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
7 authors at 3 institutions in 1 country.
Funding
Abstract
Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included.
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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.