About Us | Vector Space Biosciences

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.

Partners and collaborators

Over the years

1997

2002

2006

2007

2008

2009-2016

2017

2020

2021

2022

1997

The Bissell Lab at Lawrence Berkeley National Laboratory (LBNL/DOE) was commissioned by NASA to write a report on how to protect the human body for a planned mission to Mars.

2002

2002 forward, our team was invited to work at Lawrence Berkeley Lab's Biosciences division (formerly Life Sciences division) in collaboration with Mina J. Bissell’s lab and the NASA Ames Intelligent Systems Division’s AutoClass group. Our work was related to genes involved in extending the lifespan of nematodes (Blei DM, Franks K, Jordan MI, Mian IS), chromosomal radiation damage and DNA repair pathways in the context of space radiation. The work was supported under U.S. Department of Energy under Contract No. DE-AC02-05CH11231

Granted first patent: US20030204496A1 - "Inter-term relevance analysis for large libraries"

2006

The team publishes the paper BMC Bioinformatics - “Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span, accepted in the BMC Bioinformatics journal including others.

2007

Granted three patents: US7987191B2 - “System and method for generating a relationship network”, US20080154886A1 - “System and method for summarizing search results” and US8037051B2 - “Matching and recommending relevant videos and media to individual search engine results.”

2008

LBNL wins 4 R&D 100 Awards.

Granted two patents: US8117185B2 - “Media discovery and playlist generation” and US8108417B2 - “Discovering and scoring relationships extracted from human generated lists.”

2009-2016

Founded start-ups in industries based on our work at Lawrence Berkeley National Lab (LBNL/DOE).

2017

Vectorspace AI is founded.

2020

Within 24 hours of the first identified COVID-19 case, Vectorspace AI releases a thematic basket of stocks correlated to COVID-19, a global event. This basket resulted in significant gains that exceeded expectations.

2021

Vector Space Biosciences is founded and establishes an award winning Scientific Advisory Board (SAB).

2022

Work published with Imperial College of London (ICL) titled From Markets To Molecules