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Glycomics

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Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis.

Proceedings of the National Academy of Sciences of the United States of America
The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have...

Artificial intelligence in the analysis of glycosylation data.

Biotechnology advances
Glycans are complex, yet ubiquitous across biological systems. They are involved in diverse essential organismal functions. Aberrant glycosylation may lead to disease development, such as cancer, autoimmune diseases, and inflammatory diseases. Glycan...

Glycoinformatics in the Artificial Intelligence Era.

Chemical reviews
Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and t...

Can ChatGPT pass Glycobiology?

Glycobiology
The release of text-generating applications based on interactive Large Language Models (LLMs) in late 2022 triggered an unprecedented and ever-growing interest worldwide. The almost instantaneous success of LLMs stimulated lively discussions in publi...

Predicting glycan structure from tandem mass spectrometry via deep learning.

Nature methods
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...

Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.

Analytical and bioanalytical chemistry
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...

Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.

International journal of molecular sciences
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...

Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.

Computers in biology and medicine
An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three l...

GNOme, an ontology for glycan naming and subsumption.

Analytical and bioanalytical chemistry
While GlyTouCan provides stable identifiers for referencing glycan structures, they are not organized semantically. GNOme, a glycan naming and subsumption ontology and a member of the OBOFoundry, organizes GlyTouCan accessions for automated reasoning...

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

Journal of proteome research
The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of machine learning algorithms in diagnosing and predicting the prognosis for PCNSL based on cerebrospinal...