ArticleNature chemistry2018
A molecular multi-gene classifier for disease diagnostics.
Article in Nature chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed.
- Recurrent neural chemical reaction networks trained to switch dynamical behaviours through learned bifurcations.Journal of the Royal Society, Interface · 2026Article
- Programmable DNA Logic Systems for Applications in Biomedicine.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis.Nature communications · 2026Article
- On Degradation-Induced Leak in Heterochiral DNA Strand Displacement Cascades.ACS synthetic biology · 2026Article
- Enabling global-scale nucleic acid repositories through versatile, scalable biochemical selection from room-temperature archives.Nature communications · 2026Article
- Synthetic dynamic transcription frameworks and their applications.Nature chemistry · 2026Review
- Concept, development and applications of DNA computation.Fundamental research · 2026Review
- Hierarchical core-shell DNA condensates enable programmable information storage and encryption.Nature communications · 2025Article
- Compact RNA sensors for increasingly complex functions of multiple inputs.Nature chemistry · 2025Article
- Deep convolutional and fully-connected DNA neural networks.Nature communications · 2025Article
- Interpretable molecular decision-making with DNA-based scalable and memory-efficient tree computation.Nature communications · 2025Article
- Multiplexed and high-bandwidth DNA computing circuits with superresolution DNA origami displays.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Polymerase-based DNA reactions for molecularly computing cancerous diagnostic valences of multiple miRNAs.Journal of nanobiotechnology · 2025Article
- Supervised learning in DNA neural networks.Nature · 2025Article
- DNA Sequential Logic Circuits for Reversible Counters and Dynamic Biomolecular Sensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Pattern recognition in living cells through the lens of machine learning.Open biology · 2025Review
- DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Implementing complex nucleic acid circuits in living cells.Science advances · 2025Article
- All-in-one Biocomputing Nanoagents with Multilayered Transformable Architecture based on DNA Interfaces.Theranostics · 2025Article
- Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.Biotechnology and bioengineering · 2025Article
7 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.
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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.