Bioz, Using Machine Learning to Optimize Biology, Launches With $3M
The evolution of technology, from natural language processing to machine learning, is now helping the software world find more places to interact with biology.
A company launched today in Palo Alto, CA, that has plans to build a “life science search engine” that may be able to speed up the process of drug discovery and life sciences research. Called Bioz, the startup closed $3 million in seed funding from 5AM Ventures to begin offering its software-as-a-service system. The software, which uses machine learning, sifts through millions of pages of scientific papers to select products, help plan experiments, and perform other research-related functions, with the goal of improving the process of developing treatments for disease.
Bioz, founded by CEO Daniel Levitt and Stanford research scientist Karin Lachmi, says it will help workers select products—from reagents to instruments—they would use in research projects. For example, antibodies don’t work up to 50 percent of the time, Bioz says, and Bioz will allow researchers to find, compare, and select antibodies that would work best for their arrays and experiments.
The company also provides a rating system for products that are used in life science research, such as lab equipment, assays, instruments, and kits. The system uses an algorithm to weigh both qualitative and quantitative measures to determine the rating, the company says.
Bioz is available currently in beta trials, and is free. The company says it is using the seed funding to further develop the platform and to gain more users. Stanford’s StartX Fund, Astia Angels, and other institutional and individual investors also participated in the funding round.