We are seeking to improve scalability, interoperability, and security in digital health by addressing optimized approaches for acquisition, storage, distribution, and analytics.
We bring in computer scientists’ expertise and need to validate their work to help biologists and bioinformaticians scale research. Over the years, the lowering cost of sequencing leads to an overwhelming amount of biomedical data generated. While this helps biologists and bioinformaticians unlock deeper insights, they often find they lack the time or knowledge to acquire, store, and analyze data in a scalable way.
Our multidisciplinary team builds cost-efficient systems for research studies with a large number of participants and different types of data, with the aim to collect, store and analyze data across multiple platforms, while preserving fast turnaround times. By using robust de-identification and end-to-end encryption methods, we reduce the risk of violating privacy and security regulations.
The ability to collect and utilize more data with ease accelerates the transformation from conventional medicine to deep medicine. In conventional medicine, we are considered sick when our health status deviates from the population’s average. Deep medicine reveals the wide individual differences by investigating a variety of domains, and unlocks personalized insights to prevent diseases and manage daily health.
Hippocrates
A Greek Physician, almost 2,500 years ago
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