ArticleBMC bioinformatics2010
Learning gene regulatory networks from only positive and unlabeled data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.Current issues in molecular biology · 2026Article
- MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference.Frontiers in bioinformatics · 2026Article
- Positive and unlabeled learning from hospital administrative data: a novel approach to identify sepsis cases.Health care management science · 2025Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.BMC bioinformatics · 2024Article
- Learning peptide properties with positive examples only.Digital discovery · 2024Article
- Article
- Widening the landscape of transcriptional regulation of green algal photoprotection.Nature communications · 2023Article
- AGRN: accurate gene regulatory network inference using ensemble machine learning methods.Bioinformatics advances · 2023Article
- Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data.BioData mining · 2022Article
- Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review.Biology · 2020Article
- Dual graph convolutional neural network for predicting chemical networks.BMC bioinformatics · 2020Article
- Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.Current genomics · 2020Article
- Machine learning prediction of oncology drug targets based on protein and network properties.BMC bioinformatics · 2020Article
- Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks.Computational and structural biotechnology journal · 2020Article
- Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.Oncotarget · 2017Article
- A link prediction approach to cancer drug sensitivity prediction.BMC systems biology · 2017Article
- Reverse engineering highlights potential principles of large gene regulatory network design and learning.NPJ systems biology and applications · 2017Article
- Inference of Gene Regulatory Network Based on Local Bayesian Networks.PLoS computational biology · 2016Article
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3 authors.
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Abstract
backgroundRecently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.
resultsA recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement.
conclusionsCompared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.
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