ArticlemSystems
ADAGE-Based Integration of Publicly Available
Article in mSystems. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
69 citing papers in PubMed, 137 citations in OpenAlex.
- Artificial Intelligence for Microbial Isolation and Cultivation: Progress and Challenges.Microorganisms · 2026Review
- Multi-task adaptive deep sparse canonical correlation analysis for multi-omics cancer survival prediction.PloS one · 2026Article
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- DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.Interdisciplinary sciences, computational life sciences · 2024Article
- AutoTransOP: translating omics signatures without orthologue requirements using deep learning.NPJ systems biology and applications · 2024Article
- RNA-Seq Data Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- 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
- MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.Bioinformatics (Oxford, England) · 2023Article
- An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- PAUSE: principled feature attribution for unsupervised gene expression analysis.Genome biology · 2023Article
- The effect of non-linear signal in classification problems using gene expression.PLoS computational biology · 2023Article
- Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.Communications biology · 2023Article
- Article
- Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia.mSystems · 2023Article
- SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.Genomics, proteomics & bioinformatics · 2022Article
- Machine Learning in Drug Discovery: A Review.Artificial intelligence review · 2022Article
- Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (
Indexed as
Identifiers
What OpenQuestion holds
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.