Aomics GmbH

AI-based model development for Immuno-informatics

We develop AI algorithms for analyzing massive immunology, multi-omics as well as clinical data to innovate novel personalised immunotherapies

High-throughout data analysis facility

Our customised AI empowered immuno-informatics workflow

Experimental Data

Immunology/Clinical Data generation

Clinical trials’ data analyses

Aomics core facility:

  • Artificial Intelligence model development
  • Scientific Data mining/processing
  • Blockchain development and maintenance

Patient data collection

Omics analyses

Artificial intelligence & Machine Learning for Immuno-informatics

We develop powerful and innovative AI models and pipelines for Immuno-informatics. By leveraging AI, we assist making powerful insights from the high-throughput immunological data.

We use artificial intelligence (AI) and machine learning (ML) techniques to optimize the properties of therapeutic antibodies. Therapeutic antibodies are antibodies that are developed for use in treating diseases, such as cancer and autoimmune disorders.

Optimizing the properties of therapeutic antibodies is important for improving their efficacy and reducing potential side effects. Antibody optimization using AI involves the use of computational methods to predict the effects of changes to the antibody structure on its properties, such as its affinity for its target and its stability.

AI and ML techniques can be used to analyze large datasets of antibody structures and properties to identify patterns and relationships between different variables. These techniques can then be used to develop predictive models for optimizing antibody properties.

Immune modulators are molecules that can stimulate or suppress the immune system, and they have potential applications in the treatment of various diseases, including cancer, autoimmune disorders, and infectious diseases.

One approach for using AI in immune modulator discovery is to employ virtual screening techniques to identify molecules that can interact with specific immune system targets. Virtual screening involves using computational methods to screen large libraries of compounds to identify those that are most likely to bind to a target of interest. Machine learning algorithms can be used to predict the binding affinity of potential modulators based on their molecular structure.

Another approach is to use AI to analyze large datasets of biological and clinical data to identify potential immune modulators. For example, machine learning algorithms can be used to analyze gene expression data from patient samples to identify genes that are differentially expressed in patients with a particular disease. These differentially expressed genes could represent potential targets for immune modulator development.

AI algorithms can be used to analyze large-scale immunological data sets and predict immune responses to specific pathogens or immunotherapies.

AI can assist in the design of novel vaccines by analyzing the interaction between the immune system and pathogens, predicting potential epitopes, and optimizing vaccine formulations.

AI can assist in the identification of potential epitopes for vaccine development by analyzing large-scale immunological data sets and predicting antigenic regions of proteins.

AI can assist in the discovery of new immunomodulatory drugs by analyzing the immune system’s response to pathogens and predicting potential targets for drug development.

AI can assist in the analysis of the immune repertoire by analyzing large-scale sequencing data and identifying unique features of immune cells in response to different stimuli.

Would you like to start a project with us?

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