AIMC Topic: Mass Spectrometry
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A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.
Journal of mass spectrometry : JMS
Sep 1, 2024
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to...
On-site analysis and rapid identification of citrus herbs by miniature mass spectrometry and machine learning.
Rapid communications in mass spectrometry : RCM
Aug 30, 2024
BACKGROUND: Natural medicines present a considerable analytical challenge due to their diverse botanical origins and complex multi-species composition. This inherent complexity complicates their rapid identification and analysis. Tangerine peel, a pr...
[Research progress of deep learning applications in mass spectrometry imaging data analysis].
Se pu = Chinese journal of chromatography
Jul 1, 2024
Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total...
Deep learning method for the prediction of glycan structures from mass spectrometry data.
Nature methods
Jul 1, 2024
Deep learning framework for peak detection at the intact level of therapeutic proteins.
Journal of separation science
Jun 1, 2024
While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-varia...
pyM2aia: Python interface for mass spectrometry imaging with focus on deep learning.
Bioinformatics (Oxford, England)
Mar 4, 2024
SUMMARY: Python is the most commonly used language for deep learning (DL). Existing Python packages for mass spectrometry imaging (MSI) data are not optimized for DL tasks. We, therefore, introduce pyM2aia, a Python package for MSI data analysis with...
[Migration of Metals Contained in Laminated Films for Food Packaging].
Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan
Jan 1, 2024
Multilayer laminated films are widely used as food packaging materials. The substances contained in these films have the potential to migrate into food in contact, but the actual situation is unknown. In this study, we first determined the contents o...
Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mas...
ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.
Bioinformatics (Oxford, England)
Sep 2, 2023
MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. Howe...
Ionmob: a Python package for prediction of peptide collisional cross-section values.
Bioinformatics (Oxford, England)
Sep 2, 2023
MOTIVATION: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section...
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