AIMC Topic: Polysaccharides
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High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy.
Bioresource technology
Sep 23, 2024
Cellulose and hemicellulose are key cross-linked carbohydrates affecting bioethanol production in maize stalks. Traditional wet chemical methods for their detection are labor-intensive, highlighting the need for high-throughput techniques. This study...
Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.
International journal of molecular sciences
Sep 16, 2024
In immunoglobulin G (IgG), -glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates...
Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model.
Analytical and bioanalytical chemistry
Aug 30, 2024
Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identificat...
Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery.
International journal of biological macromolecules
Aug 27, 2024
This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central c...
Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.
Analytical and bioanalytical chemistry
Aug 24, 2024
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotati...
Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery.
Journal of controlled release : official journal of the Controlled Release Society
Aug 12, 2024
Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome P450 enzymes and particul...
Predicting glycan structure from tandem mass spectrometry via deep learning.
Nature methods
Jul 1, 2024
Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throug...
Machine learning tool as an enabler for rapid quantification of monoclonal antibodies N-glycans using fluorescence detector.
International journal of biological macromolecules
May 28, 2024
Liquid chromatography-mass spectrometry (LC-MS) is widely used for identification and quantification of N-glycans of monoclonal antibodies (mAbs), owing to its high sensitivity and accuracy. However, its resource-intensive nature necessitates the dev...
Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD).
Journal of proteome research
May 2, 2024
Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensit...
Prediction of glycopeptide fragment mass spectra by deep learning.
Nature communications
Mar 19, 2024
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the ...
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