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Artificial intelligence in the analysis of glycosylation data

BIOTECHNOLOGY ADVANCES (2022)

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Summary: Glycans play critical roles in biology and medicine, but the specificity of lectins, which are key proteins in glycans binding, has not been well-defined. In this study, machine learning algorithms and expert annotation were used to define the lectin specificity for 57 unique lectins. This research provides important insights into the complex binding features of commercially available lectins.

ACS CHEMICAL BIOLOGY (2022)

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Article Oncology

Multiomics Analysis of Spatially Distinct Stromal Cells Reveals Tumor-Induced O-Glycosylation of the CDK4-pRB Axis in Fibroblasts at the Invasive Tumor Edge

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Incorporating RNA-Seq transcriptomics into glycosylation-integrating metabolic network modelling kinetics: Multiomic Chinese hamster ovary (CHO) cell bioreactors

Lara Bezjak et al.

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DigiGlyc: A hybrid tool for reactive scheduling in cell culture systems

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Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells

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DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Subash C. Pakhrin et al.

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Mapping the glycosyltransferase fold landscape using interpretable deep learning

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Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis

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Summary: Characterizing glycans is essential for understanding cellular functions. Analyzing large glycomics datasets is challenging due to interconnected glycans, but GlyCompare offers a solution by correcting for statistical issues and facilitating comparative glycomics.

NATURE COMMUNICATIONS (2021)

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Article Cell Biology

Using graph convolutional neural networks to learn a representation for glycans

Rebekka Burkholz et al.

Summary: Glycans pose substantial challenges for computational biology due to their nonlinear and diverse nature. Sweet-Net, a graph convolutional neural network, outperforms other computational methods in predicting glycan properties and can learn representations that are predictive of organismal phenotypic and environmental properties. Additionally, glycan-focused machine learning can be used to predict viral glycan binding and discover viral receptors.

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Mechanistic and data-driven modeling of protein glycosylation

Coral Fung Shek et al.

Summary: Modulation of glycosylation is crucial in the development of therapeutic proteins, as different glycan moieties can significantly affect efficacy and safety. Computational tools, including mechanistic models and data-driven models, are used to understand and quantify the impact of glycosylation on therapeutic proteins. These tools help researchers gain insights into the glycosylation pathway and identify process levers for glycosylation.

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Glycowork: A Python package for glycan data science and machine learning

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Predicting glycosylation stereoselectivity using machine learning

Sooyeon Moon et al.

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The international glycan repository GlyTouCan version 3.0

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Summary: Glycans play crucial roles in signaling events and cell-to-cell communication, but organizing the vast amount of related data has been a challenge. GlyTouCan was developed as an international glycan repository to address this issue, providing researchers with a platform to assign accession numbers to glycans.

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Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction

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