CD BioGlyco is using artificial intelligence (AI) to transform glycoscience research. With a strong foundation in glycobiology and an innovative approach to technology, we provide specialized services to meet the personal needs of researchers in academia and industry.

Advanced AI Technology and Customized Solutions

CD BioGlyco provides advanced AI-assisted glycoinformatics development services for our clients to enhance glycoinformatics research. Our service consists of two main components: machine learning (ML)-assisted glycoinformatics model development service and deep learning (DL)-assisted glycoinformatics model development service. In addition, we also provide custom glycoinformatics annotations based on our clients' needs.

ML-assisted Glycoinformatics Model Development Service

Our glycoinformatics modeling service uses classical ML techniques to analyze and interpret glycan data by algorithms such as Random Forests and support vector machines (SVMs) can enable efficient classification and prediction tasks in glycoinformatics. These models can be used to optimize processes such as mass spectrometry data interpretation and glycosylation site prediction, promoting the extraction of meaningful patterns from experimental glycan data.

DL-assisted Glycoinformatics Model Development Service

Our DL models focus on using sophisticated neural networks to address more complex and subtle glycoinformatics challenges, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Based on the large datasets and high-dimensional data representations, our models can improve the accuracy of predictions of glycan position, glycan binding specificity, and other glycan-related properties.

Publication

Frequently Asked Questions (FAQs)

• What is the difference between AI and ML?
  • AI is a broad field focused on creating systems that mimic human intelligence through tasks such as reasoning, learning, and problem-solving. ML is a subset of AI that specifically involves developing algorithms that enable computers to learn from data and improve their performance over time. While AI encompasses a variety of techniques designed to mimic human abilities, ML primarily involves creating models that learn from data and make predictions based on that data.

• How to develop the ML for glycoscience data analysis?

Applications

• AI-assisted services are designed to provide a comprehensive solution for glycoinformatics.
• ML and DL methods enable researchers to obtain more accurate and insightful results in the field of glycomics.

Advantages

• AI techniques significantly improve the processing and analysis of complex glyco-data.
• AI methods enhance the accuracy of predictions related to glycosylation sites and glycan-binding specificities. DL models, in particular, can use raw sequences and integrate additional contextual information, leading to better performance compared to traditional methods.
• The use of DL allows for the development of sophisticated neural network architectures, such as CNNs and RNNs.

CD BioGlyco is committed to advancing glycoscience through innovative AI-assisted glycoinformatics development services. Contact us today to discover how we can support your research and development needs.

• Artificial Intelligence-assisted Glycan-related Drug Discovery Service
• Glycoinformatics-assisted Glycan-Molecular Interaction Analysis Service
• Glycoinformatics-guided Glycan Synthesis Service
• Glycoinformatics-assisted Analysis/Prediction Service
• Mathematical Modeling Service for Glycosylation Pathway
• Glycoinformatics-assisted Glycomics Analysis Service
• Computational Glycoengineering-assisted Specific Modeling Service