Glycan-related Biomarker Discovery Service
Artificial Intelligence (AI) Aids in Glycan-related Drug Discovery and Biomarker Development
AI Technologies are playing a bigger role in the Discovery of Glycan-based Drugs, biomarkers, etc. Glycans are complex glycans (CH2O) that have an essential role in numerous processes of life because they affect protein folding and stabilization, as well as cell-cell interactions. As a result, glycans are now recognized as key players in biomedical research. The discovery of glycan biomarkers is useful in the detection and diagnosis of cancer as well as other complex diseases. For one such instance, glycan microarray technology is shown as a potential tool for the biomarker discovery of cancer diagnosis and prognosis in comparison with antibody-sandwich immunoassay techniques. Furthermore, numerous clinical prognostic biomarkers were also identified utilizing deep learning (DL) techniques in liver cancer diagnosis. Protein glycosylation and glycoinformatics have also known potential in the assessment of biomarkers for neurodegenerative diseases.
Data mining and analysis
Tens of thousands of glycan data are analyzed to establish correlations with specific disease conditions, enabling the identification of disease-related glycan patterns.
Predictive models
We develop the predictive model using machine learning (ML) algorithms. Given that it can be exact in predicting the relation of diseases with certain structures.
Image recognition
AI is utilized for the identification and interpretation of complex glycan structures automatically to streamline the quantification of glycans reported by microarrays.
Optimizing experimental design
We identify those glycan structures most likely to be clinically relevant biomarkers and improve experiments with higher yields of novel biomarker sets.
Publication
DOI: 10.1016/ j.isci.2023.108715
Technology: DL and high-throughput technology
Journal: iScience
Published: 2024
IF: 5.8
Results: The findings of this study demonstrate that DL enhances the value of the serum glycome in the diagnosis of various cancers. The research utilized large-scale serum samples and high-throughput technology to analyze N-glycan and glycan-derived features, establishing DL models to predict diagnostic outcomes for multiple cancers. The study found that these models exhibited high accuracy in diagnosing ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer.
Fig.1 DL enhanced the diagnostic merit of serum glycome for multiple cancers. (Zhang, et al., 2024)
Frequently Asked Questions (FAQs)
- What are the AI technologies used in the discovery of glycan-related biomarkers?
- ML and DL: Large glycomics data is analyzed to identify glycan patterns and build predictive models.
- Data mining and big data analysis: Large-scale glycomics datasets is processed and analyzed to extract valuable information.
- Image recognition and computer vision: CNNs are used in automatically recognize and interpret complex glycan structures.
- Natural language processing (NLP): Additional insights is provided by understand textual information in scientific literature.
- What are the advantages of AI in glycan-related biomarker discovery?
- AI enables high-throughput data processing, improves accuracy, and can establish predictive models to guide drug development and treatment. It achieves automation and efficiency enhancement, aids in the discovery of new biomarkers, promotes the development of personalized medicine, reduces drug development costs, fosters interdisciplinary integration, enhances data interpretability, and provides real-time monitoring and clinical decision support. Consequently, AI accelerates the process of drug discovery and disease diagnosis.
Applications
- The glycan-related biomarker discovery service can be used to identify glycan biomarkers for various cancers.
- The glycan-related biomarker discovery service can be used to discover glycan patterns associated with Alzheimer's diseases and Parkinson's diseases.
- The glycan-related biomarker discovery service can be used to find glycan structures that indicate infection or immune response.
Advantages
- AI accelerates data processing and analysis.
- AI-driven models and analyses enhance the precision of biomarker identification.
- AI solutions can handle large-scale data, making the service suitable for extensive research projects.
At CD BioGlyco, our service provides researchers and medical professionals with powerful tools to advance the understanding and treatment of various diseases. Please feel free to contact us for more information.
Reference
- Zhang, H.; et al. Deep learning enhanced the diagnostic merit of serum glycome for multiple cancers. iScience. 2024, 27(1).
For research use only. Not intended for any diagnostic use.
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