ROSEMONT, Ill., Dec. 03, 2018 /PRNewswire-PRWeb/ -- Proteintech, the benchmark in antibodies since 2001, and BenchSci, a life science machine learning startup backed by Google's AI fund, have announced a new partnership to accelerate science by overcoming a key challenge that biomedical researchers face selecting antibodies.
When researchers use antibodies that lack specificity or yield inferior results in specific applications, it wastes time, money, and sometimes samples. This can delay research projects, the publication of research results, and ultimately the development of new therapies.
To prevent this, researchers review scientific papers for published data on antibodies when planning experiments. But more than 38 million scientific papers have been published since 1980, a number that doubles every nine years. It can take researchers hours, and sometimes days, to find useful data—if they can find it at all.
Proteintech's partnership with BenchSci aims to solve this problem by combining the company's catalog with BenchSci's industry leading artificial intelligence platform. BenchSci will use machine learning to identify published data for Proteintech's catalog in open- and closed-access datasets, display related published figures, and allow researchers to search by important experimental variables.
"BenchSci's platform is rocket fuel for the advancement of science," says Will Olds, Scientific Officer at Proteintech. "The ability to quickly comb over millions of pieces of data and find only the relevant data for a specific biological question allows scientists to make better decisions faster than ever before. We are excited to join forces with them to help accelerate scientific discovery."
"Proteintech's dedication to accelerating research with high-quality, reliable reagents mirrors our dedication to accelerating research with machine learning," says Liran Belenzon, CEO, BenchSci. "We're excited to partner with Proteintech and increase discoverability of its products by researchers in more than 1,300 academic institutions and 15 top pharmaceutical companies worldwide."
About Proteintech
Proteintech is an antibody and human protein manufacturer selling directly to scientists and only selling its own products to maintain the highest level of quality control leading to superior reproducibility and lot consistency. With over 2/3 of the human genome completed, we have a catalog of over 12,000 antibodies against 12,000 targets covering a wide spectrum of research areas. We have been cited in tens of thousands of publications and provide in-house validation of our antibodies in WB, IHC, IF, FC, IP, and CHIP, in addition to performing knock-down siRNA validation of our antibodies. Additionally, we provide human cell-expressed proteins that have authentic glycosylation and folding, yielding higher activity and stability over proteins from other hosts. To learn more about Proteintech and their mission to deliver products directly from their bench to the researcher's bench, please visit http://www.ptglab.com.
About BenchSci
BenchSci decodes the world's biological data to reduce the time, uncertainty, and cost of biomedical research. Researchers waste time and money on antibodies that don't work. This slows studies, destroys samples, and leaves patients waiting for treatments. Artificial intelligence can solve the problem, and BenchSci is building it. BenchSci's technology helps researchers find reliable antibodies 24x faster and 75% cheaper. Backed by Google's AI fund, BenchSci applies machine learning to decode comprehensive open- and closed-access datasets, display published figures with no commercial bias, and allow researchers to search by important experimental variables. BenchSci now powers discoveries in 15 of the top 20 pharmaceutical companies and 1,300 academic institutions. Academic researchers can sign up free at http://www.benchsci.com. Industry researchers can request a demo at http://www.benchsci.com/contact/.
Contact: Simon Smith, Chief Growth Officer, BenchSci, simon(at)benchsci(dot)com, 416-738-6058.
SOURCE BenchSci
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