SynthesisScientific reports2018
Machine learning for the meta-analyses of microbial pathogens' volatile signatures.
Synthesis in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 25 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
25 citing papers in PubMed.
- Measurement of immune cell-derived volatile organic compounds from ex vivo and in vitro cultures: a scoping review.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis.Scientific reports · 2026Article
- Machine Learning in FTIR Spectrum for the Identification of Antibiotic Resistance: A Demonstration with Different Species of Microorganisms.Antibiotics (Basel, Switzerland) · 2024Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Machine learning for microbiologists.Nature reviews. Microbiology · 2024Review
- Evaluating algorithms of decision tree, support vector machine and regression for anode side catalyst data in proton exchange membrane water electrolysis.Scientific reports · 2023Article
- Fecal Volatile Metabolomics Predict Gram-Negative Late-Onset Sepsis in Preterm Infants: A Nationwide Case-Control Study.Microorganisms · 2023Article
- From the nose to the lungs: the intricate journey of airborne pathogens amid commensal bacteria.American journal of physiology. Cell physiology · 2022Review
- Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS.Metabolites · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- Chemical composition: Hearing insect defensive volatiles.Patterns (New York, N.Y.) · 2021Article
- Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Induced volatolomics of pathologies.Nature reviews. Chemistry · 2021Review
- Measurement of Volatile Compounds for Real-Time Analysis of Soil Microbial Metabolic Response to Simulated Snowmelt.Frontiers in microbiology · 2021Article
- Morphological Effects in SnOSensors (Basel, Switzerland) · 2020Review
- Novel biotechnological approaches for monitoring and immunization against resistant to antibiotics Escherichia coli and other pathogenic bacteria.BMC veterinary research · 2020Article
- Microfluidics in Gas Sensing and Artificial Olfaction.Sensors (Basel, Switzerland) · 2020Review
- Sustainable plant polyesters as substrates for optical gas sensors.Materials today. Bio · 2020Article
- Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile.Biosensors · 2020Review
- Emerging coronavirus diseases and future perspectives.Virusdisease · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.
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.