Apheris
Apheris securely connects proprietary life sciences data while preserving confidentiality and IP, allowing for collaborative AI model development.
Our numbers
Apheris is designed to transform AI model performance in the life sciences sector by facilitating the secure connection of proprietary data across various organizations. This solution allows multiple parties to collaboratively train robust machine learning models while protecting the confidentiality and intellectual property (IP) of the shared data. By participating in federated data networks, organizations can utilize diverse data sets stored in private repositories and overcome the limitations of public domain data, which often lacks the variety needed for accurate and generalizable models. Apheris is particularly focused on facilitating advanced research in areas like drug discovery, working with notable biopharma companies to collaboratively develop predictive models without exposing sensitive information.
Pros:
- Enhances AI model performance in life sciences through data collaboration
- Ensures data confidentiality and IP protection
- Facilitates participation in federated data networks
- Fosters innovative drug discovery research
Cons:
- Requires collaboration with multiple parties, which may be challenging to coordinate
- Dependent on the willingness of organizations to share data
- Complex setup for integrating existing data and privacy protocols
Features:
- Federated Data Networks - Connect proprietary data while maintaining privacy and IP.
- Granular Data Privacy Controls - Maintain control over data access and model training.
- Seamless Tool Integration - Compatible with existing tools and protocols for easy integration.
- AI Model Customization - Customize globally trained models to meet specific needs.
Integrations:
- NVIDIA - Partner in advancing AI-driven drug discovery through computational power.
- Fraunhofer - Collaborator in research and development projects.
Pricing:
- Enterprise solutions - Custom agreements based on organization needs and data management scope
Implementation Timeline
Not Disclosed
Onboarding
Not Disclosed
Sandbox
No
Compliance
Not Disclosed
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