ArticleCell systems2017
Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.
Article in Cell systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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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.
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.
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Who cites it
55 citing papers in PubMed.
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
- Roles of Pho regulon in bacterial pathogenicity.Virulence · 2025Review
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Article
- Machine learning tools for deciphering the regulatory logic of enhancers in health and disease.Frontiers in genetics · 2025Review
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
- Article
- Network depth affects inference of gene sets from bacterial transcriptomes using denoising autoencoders.Bioinformatics advances · 2024Article
- CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model.Briefings in bioinformatics · 2023Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Reliable interpretability of biology-inspired deep neural networks.NPJ systems biology and applications · 2023Article
- Analysis ofbioRxiv : the preprint server for biology · 2023Article
- Spatial mapping of the DNA adducts in cancer.DNA repair · 2023Review
- PAUSE: principled feature attribution for unsupervised gene expression analysis.Genome biology · 2023Article
- Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia.mSystems · 2023Article
- Article
- SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.Genomics, proteomics & bioinformatics · 2022Article
- Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice.Biomedicines · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.
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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.