Artificial Intelligence (AI) Decodes Glycan-disease Relationships

At CD BioGlyco, our glycan correlation computational pathology services use state-of-the-art AI Technology and high-throughput data processing to decode complex glycans associated with disease conditions. Methods for high-throughput data processing have now been developed to inform on N-glycan signatures in unprecedented detail. In general, convolutional neural networks (CNN) as well as other deep learning algorithms can be used for automated analysis of glycans in microarray studies. Further, through natural language processing (NLP), we integrate and analyze scientific literature with reports to complete our comprehension of this field in computational pathology.

Here is an overview of the key steps involved in our glycan-related computational pathology process: data collection, image annotation, feature extraction, model training and validation, deployment, model iteration, quality control, and custom user interaction. Moreover, clients can also find other Glycan-related Drug Discovery Services on our website.

Data collection: A store of pathological slide images marked with glycosylation, together with patient data that also includes diagnostic developments and therapeutic responses.

Image annotation: Pathology experts annotate the slide images to identify areas of interest such as those that express glycosylated proteins or enzymes by zooming in on specific image features.

Feature extraction: We apply computer vision to automatically determine glycosylation-related image features in pathological slides (in terms of shape, color, texture, etc.).

Model training: For predictions of glycosylation states, we create a set of machine learning (ML) models that are trained with the annotated data and obtained image features.

Model validation: Validate model accuracy, sensitivity, and specificity of other metrics on independent test sets by optimizing to an appropriate performance metric.

Deployment: To measure the glycosylation ratio, users can upload pathological images and receive an automatic analysis report.

Model iteration: Gather more and better data from user uploads for ongoing ML training to improve model results over time.

Quality control: Set up quality control to validate the stability, accuracy, and calibration of your model iteratively with known samples.

User interaction: Easy-to-use interface to capture the report, analyze, and communicate with pathology experts.

Publication

Applications

• Computational pathology is used to guide the development of glycan-based therapeutics and identify potential drug targets.
• Computer algorithms are used to automate the detection, typing, and grading of tumor tissue samples.
• Feature extraction and analysis of pathology images using computer vision and image analysis techniques to help physicians better understand cellular structures and tissue features in pathology samples.

Advantages

• High-throughput data processing ensures efficient analysis, while AI-driven models enhance the precision of biomarker discovery.
• Automated workflows streamline the discovery process, and scalable solutions accommodate extensive research projects.
• Advanced AI techniques improve the interpretability of complex glycemic data, aiding in better decision-making.
• Providing real-time insights support to accelerate drug discovery and disease diagnosis.

Frequently Asked Questions (FAQs)

• What is the relationship between abnormal carbohydrate modification and the occurrence and development of diseases?
  Glycosylation is the process of adding carbohydrate molecules to proteins, lipids, and other biomolecules and plays a key role in various biological functions. When glycosylation is disrupted, it may lead to abnormalities in cell function and signaling, which can lead to the development of various diseases.
• What are the potential applications of carbohydrates in disease diagnosis and treatment?
  Glycans have great potential in disease diagnosis and treatment, serving as biomarkers for early detection of cancer and congenital glycosylation disorders. In addition, glycan analysis enables personalized medicine by tailoring treatment regimens to individual glycan patterns and monitoring treatment effects.

At CD BioGlyco, our glycan-related computational pathology services are designed to push the boundaries of biomedical research and clinical diagnostics, delivering innovative solutions for improved healthcare outcomes. Please feel free to contact us for more information and custom solutions.

• Glycan-related Target Identification and Validation Service
• Glycan-related Small-molecule Design and Optimization Service
• Glycan-related Biomarker Discovery Service
• Glycan-related Molecular Simulation Service
• Candidate Glycan-related Drug Prioritization Service
• Glycan-related High-throughput Virtual Drug Screening Service
• Glycan-related Drug Transmembrane Osmosis Analysis Service
• Glycan-related Drug Property Prediction Service
• De Novo Glycan-related Drug Design Service
• Multiple Glycan Targeting-related Drug Design Service
• Glycan Structure-based Drug Design Service
• Glycan-related Synthesis Pathway Generation Service