Our mission is to detect hidden relationships in data to fuel and accelerate new insights, interpretations, hypotheses and novel discoveries in space biosciences including materials and chemical sciences for the benefit of all humankind.
Our team is composed of a network of computational labs and bioscience labs along with scientific advisors from areas including space biosciences, general life sciences and the capital markets, all of whom share a common background in AI and ML applications. Our scientific data engineering pipelines based on ensembled language models resulting in real-time datasets enable signal detection, prediction and advanced decision-making for any institution or enterprise using data to drive discovery and innovation.
For centuries, the hieroglyphs on ancient Egyptian temples remained indecipherable until the discovery of the Rosetta Stone in 1799. A decree carved on a granite-like stele written in three languages: Ancient Egyptian hieroglyphics, Ancient Egyptian demotic, and Ancient Greek. A 1,680 pound cipher key, the Rosetta Stone along with techniques associated to epigraphy, enabled deciphering of the Ancient Egyptian languages, providing access to a vast trove of hidden knowledge.
This cipher key was critical to deciphering hidden relationships in what was thought a lost language. But imagine a cipher key that uses inputs to detect relationships within a specific set of data: a customizable tool that identifies hidden correlations in the immense library of scientific research that multiplies exponentially daily.
Vector Space Biosciences exists to solve a series of related problems in space exploration which cannot flourish until the damaging effects of stressors, including microgravity and radiation, on the human body can be protected and repaired. Utilizing our scientific data engineering pipelines powered by ensembled language models, developed over twenty-plus years, our proven methodology transforms the massive volume of new and existing research into real-time, visualized, correlation matrix datasets, detecting hidden relationships that can be applied to any area or aspect of bioscience research, resulting in the acceleration of new insights, hypotheses, interpretations and novel discoveries.
