Scientific Reports · 2016 · 67 citations · 25 references
Life SciencesInfectious DiseasesMicrobiologyDiagnostic MicrobiologyMass Spectrometric AnalysisMedical MicrobiologyFungal DiagnosticsFungal IdentificationFungal InfectionsMass SpectrometryPathogen DetectionFungal DetectionMaldi-tof IdentificationMaldi-tof Mass SpectrometryMass Spectrometry-based IdentificationMatrix-assisted Laser Desorption/ionizationMatrix-assisted Laser Desorption/ionization Time-of-flight Mass SpectrometryRapid Fungal DiagnosticsSpecies Classification
Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value < 0.05) lipids within the 600 to 1000 m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities.
25
Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks
Paul Shannon, Andrew Markiel, Owen Ozier et al. · Genome Research · 2003 · 54.3K citations · Full text
MetaboAnalyst 3.0—making metabolomics more meaningful
Jianguo Xia, Igor Sinelnikov, David S. Wishart · Nucleic Acids Research · 2015 · 2.7K citations · Full text