AIMC Topic: Glycomics
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Can ChatGPT pass Glycobiology?
Glycobiology
Oct 6, 2023
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...
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
Mar 16, 2021
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...
GlyGen data model and processing workflow.
Bioinformatics (Oxford, England)
Jun 1, 2020
SUMMARY: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access i...
IMass Time: The Future, in Future!
Omics : a journal of integrative biology
Nov 1, 2018
Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been...
A Machine Learning Based Approach to de novo Sequencing of Glycans from Tandem Mass Spectrometry Spectrum.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 1, 2015
Recently, glycomics has been actively studied and various technologies for glycomics have been rapidly developed. Currently, tandem mass spectrometry (MS/MS) is one of the key experimental tools for identification of structures of oligosaccharides. M...
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