Context

Over the past 50 years, we have gained significant insights into all levels of cellular processes. It has becoming overwhelmingly clear that the etiology of many diseases, especially cancer is more complex and dynamic than we have ever imagined. Our need to understand these complex systems has driven technology innovation to help us acquire not only greater but more fine-grained information about diseases. Most notably, the advancements in sequencing technology have pushed the biomedical field into a new level of access and quantity to available data about the human genome, and thereby opened up uncharted space for precision medicine discovery. However, data is growing at a much greater speed than the technological tools that can harness useful insights, therefore slowing down the application of knowledge that can improve patient care.

We are creating a solution that would reduce bottlenecks during the insights generation process, by building a democratized platform that can extract from raw molecular data targets that can be used to inform and design therapeutics, leveraging modern age advances in machine learning.