ReviewACS omega2020
Combination of Single-Molecule Electrical Measurements and Machine Learning for the Identification of Single Biomolecules.
Review in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Optimization of the Quantum-Si Platinum Single-Molecule Protein Sequencing Platform Toward Improved Complex-Matrix Protein Identification.ACS omega · 2026Article
- Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges.Precision chemistry · 2024Review
- Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors.Bioengineering (Basel, Switzerland) · 2024Review
- Machine learning empowered next generation DNA sequencing: perspective and prospectus.Chemical science · 2024Review
- Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.ACS sensors · 2023Article
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
- Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.Nanoscale · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of a next-generation DNA sequencer has provided a method for electrically measuring single molecules. Methods for electrically measuring one molecule are roughly divided into methods for measuring tunneling and ion currents. These methods enable identification of a single molecule of DNA, a RNA nucleotide, or a single protein based on current histograms. However, overlapping of current histograms of molecules with similar properties has been a major barrier to identifying single molecules with high accuracy. This barrier was broken by introducing machine learning. Combining single-molecule electrical measurement and machine learning enables high-precision identification of single molecules. Highly accurate discrimination has been demonstrated for DNA nucleotides, RNA nucleotides, amino acids, sugars, viruses, and bacteria. This combination enables quantitative evaluation of molecular recognition ability. Furthermore, a device structure suitable for high-precision identification has been designed. Combining single-molecule electrical measurement with machine learning enables digital analytical chemistry that can count certain types of molecules. Digital analytical chemistry enables comprehensive analysis of chemical reactions. This new analytical method will lead to the discovery of unknown or missed valuable molecules.
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.