AIMC Topic: Tandem Mass Spectrometry
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Deep learning for peptide identification from metaproteomics datasets.
Journal of proteomics
Jul 8, 2021
Metaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled wi...
Evaluation of external contamination on the vial surfaces of some hazardous drugs that commonly used in Chinese hospitals and comparison between environmental contamination generated during robotic compounding by IV: Dispensing robot vs. manual compounding in biological safety cabinet.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Jun 24, 2021
OBJECTIVES: The aims of the study were to evaluate the external contamination of hazardous drug vials used in Chinese hospitals and to compare environmental contamination generated by a robotic intelligent dispensing system (WEINAS) and a manual comp...
Assessment of lemon juice quality and adulteration by ultra-high performance liquid chromatography/triple quadrupole mass spectrometry with interactive and interpretable machine learning.
Journal of food and drug analysis
Jun 15, 2021
A total of 81 lemon juices samples were detected using an optimized UHPLC-QqQ-MS/MS method and colorimetric assays. Concentration of 3 organic acids (ascorbic acid, malic acid and citric acid), 3 saccharides (glucose, fructose and sucrose) and 6 phen...
A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.
Scientific reports
Jun 15, 2021
Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphoryl...
Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.
Nature communications
Jun 7, 2021
Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational...
Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.
Nature communications
May 28, 2021
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...
Deep learning neural network tools for proteomics.
Cell reports methods
May 17, 2021
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategie...
Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.
Molecules (Basel, Switzerland)
May 5, 2021
Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discrimi...
pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning.
Journal of proteome research
Apr 6, 2021
In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accura...
SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics.
Analytical chemistry
Mar 30, 2021
Despite the vast amount of metabolic information that can be captured in untargeted metabolomics, many biological applications are looking for a biology-driven metabolomics platform that targets a set of metabolites that are relevant to the given bio...
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