ArticlePLoS computational biology2008
A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.
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 42 papers.
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Who cites it
42 citing papers in PubMed, 105 citations in OpenAlex.
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- Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.Molecules (Basel, Switzerland) · 2022Article
- Constructing gene regulatory networks using epigenetic data.NPJ systems biology and applications · 2021Article
- Computational biology approaches for mapping transcriptional regulatory networks.Computational and structural biotechnology journal · 2021Review
- Molecular networks in Network Medicine: Development and applications.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
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- High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering.Frontiers in genetics · 2019Article
- Gene regulatory network construction identified NFYA as a diffuse subtype-specific prognostic factor in gastric cancer.International journal of oncology · 2018Article
- Single-cell RNA sequencing technologies and bioinformatics pipelines.Experimental & molecular medicine · 2018Review
- Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer.Cancer research · 2018Article
- Prior Knowledge Driven Joint NMF Algorithm for ceRNA Co-Module Identification.International journal of biological sciences · 2018Article
- Gene regulatory pattern analysis reveals essential role of core transcriptional factors' activation in triple-negative breast cancer.Oncotarget · 2017Article
- PyPanda: a Python package for gene regulatory network reconstruction.Bioinformatics (Oxford, England) · 2016Article
- Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.Nature communications · 2016Article
- Inference of Gene Regulatory Network Based on Local Bayesian Networks.PLoS computational biology · 2016Article
- Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.BMC medical genomics · 2016Article
- Taking Bioinformatics to Systems Medicine.Methods in molecular biology (Clifton, N.J.) · 2016Review
- Regulatory network inferred using expression data of small sample size: application and validation in erythroid system.Bioinformatics (Oxford, England) · 2015Article
- SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor-gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor-gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic-anaerobic shift interface.
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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.