AIMC Topic: Mass Spectrometry

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Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules.

Analytical chemistry Nov 15, 2021
Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of ...
Machine Learning Algorithms Mass Spectrometry Neural Networks, Computer
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T Cell Epitope Prediction and Its Application to Immunotherapy.

Frontiers in immunology Sep 15, 2021
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for ep...
Histocompatibility Antigens Class I Base Sequence Receptors, Antigen, T-Cell Antigen Presentation T-Cell Antigen Receptor Specificity Humans Mass Spectrometry Immunotherapy Neoplasms Computational Biology Antigens, Neoplasm Models, Molecular Epitopes, T-Lymphocyte Neural Networks, Computer Sequence Analysis, DNA Peptides
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EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.

Analytical chemistry Aug 28, 2021
Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a sign...
Algorithms Deep Learning Mass Spectrometry Metabolomics Chromatography, Liquid
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Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

Molecules (Basel, Switzerland) Jun 23, 2021
The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradation products such as particulate wear and corrosion debris, metal-protein complexes, free metallic ion...
Ions Metals, Heavy Corrosion Autopsy Cobalt Mass Spectrometry Robotics Chromium Titanium Nickel Prostheses and Implants Humans Aluminum
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Investigation and Highly Accurate Prediction of Missed Tryptic Cleavages by Deep Learning.

Journal of proteome research Jun 17, 2021
Trypsin is one of the most important and widely used proteolytic enzymes in mass spectrometry (MS)-based proteomic research. It exclusively cleaves peptide bonds at the C-terminus of lysine and arginine. However, the cleavage is also affected by seve...
Mass Spectrometry Trypsin Peptides Deep Learning Proteomics
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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...
Humans Histocompatibility Antigens Class II Molecular Medicine HLA Antigens Epitopes Histocompatibility Antigens Class I Tandem Mass Spectrometry Ligands Extracellular Matrix Proteins Proteomics Peptides Mass Spectrometry Deep Learning Cell Line
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Profiling of contemporary beer styles using liquid chromatography quadrupole time-of-flight mass spectrometry, multivariate analysis, and machine learning techniques.

Analytica chimica acta May 24, 2021
Although all beer is brewed using the same four classes of ingredients, contemporary beer styles show wide variation in flavor and color, suggesting differences in their chemical profiles. A selection of 32 beers covering five styles (India pale ale,...
Beer Chromatography, Liquid Multivariate Analysis Machine Learning Mass Spectrometry
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Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations.

Analytica chimica acta Apr 26, 2021
The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impract...
Machine Learning Biomedical Research Animals Mice Rats Mass Spectrometry Diagnostic Tests, Routine
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Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data.

International journal of molecular sciences Apr 15, 2021
High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as mole...
Cluster Analysis Principal Component Analysis Lignin Algorithms Pyrolysis Machine Learning Biomass Mass Spectrometry
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Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

PLoS computational biology Mar 29, 2021
Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous prote...
Molecular Imaging Image Interpretation, Computer-Assisted Image Cytometry Algorithms Databases, Factual Lung Neoplasms Humans Antineoplastic Agents Mass Spectrometry Machine Learning
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