ArticleBriefings in bioinformatics2014
Supervised, semi-supervised and unsupervised inference of gene regulatory networks.
Article in Briefings in bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery.Frontiers in pharmacology · 2024Pooled it
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- PGBTR: a powerful and general method for inferring bacterial transcriptional regulatory networks.BMC genomics · 2025Article
- Informeasure: an R/bioconductor package for quantifying nonlinear dependence between variables in biological networks from an information theory perspective.BMC bioinformatics · 2024Article
- Article
- DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.Biomolecules · 2024Article
- Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs.Interdisciplinary sciences, computational life sciences · 2024Article
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.BMC bioinformatics · 2023Article
- AGRN: accurate gene regulatory network inference using ensemble machine learning methods.Bioinformatics advances · 2023Article
- Knowledge of the perturbation design is essential for accurate gene regulatory network inference.Scientific reports · 2022Article
- Single_cell_GRN: gene regulatory network identification based on supervised learning method and Single-cell RNA-seq data.BioData mining · 2022Article
- Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm.Interdisciplinary sciences, computational life sciences · 2022Article
- Detecting Depression Signs on Social Media: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- COSIFER: a Python package for the consensus inference of molecular interaction networks.Bioinformatics (Oxford, England) · 2021Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.
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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.