All Articles by Marie Roehm
What Are Collaborative Data Ecosystems in the Context of PharmaCos?
Collaborative data ecosystems in pharma securely connect life sciences and healthcare data to drive AI-powered insights or advanced analytics on these distributed datasets. They enable shared use of real-world data without exposing sensitive information, overcoming data silos while ensuring privacy and compliance.
The Rise of Federated Learning: Top 5 Federated Learning Startups
Companies are benefiting from federated learning by being able to access data without privacy and IP risks associated with copying or centralizing data.
Securing ML Models: Apheris' Contribution to ML Security
Together with the German Federal Office for Information Security we've developed frameworks and recommendations for ML practitioners to help secure ML models and maintain appropriate security measures.
AlQuraishi lab’s OpenFold3 to Be Fine-Tuned with Pharma Industry Data in a Secure AI Collaboration Powered by Apheris
OpenFold3, a structure prediction system developed by AlQuraishi Lab at Columbia University, will be fine-tuned using proprietary data from AbbVie and Johnson & Johnson in a confidentiality-preserving and secure federated environment powered by Apheris.
Apheris announces Series A fundraise to power the world’s leading life sciences data networks
Apheris has closed a Series A funding round, raising total funding to $20.8M. Led by OTB Ventures and eCAPITAL, this round will help Apheris to expand its secure life sciences data networks.
Data collaboration vs data sharing: Everything you should know
Discover why data collaboration is the future. Explore how federated learning keeps data safe while promoting collaboration without sharing.
Governed, secure, and private computational access to federated patient data is revolutionizing healthcare AI
MedTech AI revolution depends on computational access to fine-grained patient data to train enhanced ML models. Apheris provides governed, secure, and private computational access to data for ML, enabling fast and easy FDA approval for new MedTech data products.
The Three Adoption Stages of Privacy-enhancing Technologies (And Why We Are Stuck on Level Two)
PETs are massively changing how we operate, and how we have to think about the data and AI landscape. Introducing such a game-changer into large enterprises has to be done with the highest precision, and a lot of foresight.
7 Myths About Federated Learning
In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.
How to Choose the Best Federated Learning Platform
How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.
7 Value Drivers of Complementary Data - Your Path to Unlocking the Benefits of Federated Data
There is only value in data if it can be used, and if there is appropriate access to that data when it is needed. Learn how you can turn decentralized, federated data into a complementary strategic data asset.
Celebrating women's contribution to STEM fields
11 February is the International Day of Women and Girls in Science. We interview Evelyn, Data Science Experience Lead at Apheris, and give you an insight into the life of a successful woman in science.