The technological advances of the past century, marked by the computer revolution and the
advent of high-throughput screening technologies in drug discovery, opened the path to the …

Artificial intelligence (AI) methods have been and are now being increasingly integrated in
prediction software implemented in bioinformatics and its glycoscience branch known as …

Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from
host-microbe interactions. Here, we present machine learning and bioinformatics methods to …

Glycans are essential to all scales of biology, with their intricate structures being crucial for
their biological functions. The structural complexity of glycans is communicated through …

The mass spectrometry (MS)-based analysis of free polysaccharides and glycans released
from proteins, lipids and proteoglycans increasingly relies on databases and software. Here …

Glycans are known as the third major class of biopolymers, next to DNA and proteins. They
cover the surfaces of many cells, serving as the 'face'of cells, whereby other biomolecules …

Molecular similarity pervades much of our understanding and rationalization of chemistry.
This has become particularly evident in the current data-intensive era of chemical research …

While glycans are crucial for biological processes, existing analysis modalities make it
difficult for researchers with limited computational background to include these diverse …

Resource description framework (RDF) and Property Graph databases are emerging
technologies that are used for storing graph-structured data. We compare these …

Studying glycans and their functions in the body aids in the understanding of disease
mechanisms and developing new treatments. This necessitates resources that provide …