Thermo Fisher Scientific has signed an agreement with reagent intelligence platform BenchSci to use its trademarked machine learning to mine antibody data published in peer-reviewed scientific journals and display it alongside associated application information on product-specific webpages.
Thermo Fisher aims for this collaboration to help scientists make informed decisions about the optimal antibodies to use in experiments.
Researchers have previously had to rely on other methods when choosing antibodies such as using scientific search engines and sifting through published papers. This could take hours or even days but Thermo Fisher believes it has reduced the process time to minutes by extracting key information and figures from open- and closed-access papers.
An image gallery on the company’s antibody product pages will incorporate data generated by BenchSci’s platform so website visitors can view internal product development data and peer-reviewed journals’ figures, all in one location. Additional published figures will be added over time.
Critical research delays can be caused by poor antibody specificity or application performance as these issues significantly hinder the ability to obtain good results. Choosing the wrong antibody or one that underperforms can result in a lack of reproducibility, wasted time and wasted resources, Thermo Fisher said in a news release. Therefore, researchers need antibodies that bind to the right target and work in their applications every time.
Thermo Fisher Scientific vice president and general manager of protein and cell analysis Dara Wright said: “Data is absolutely critical to ensuring that scientists can make high confidence decisions about what antibody reagent is likely to be most appropriate for their application of interest. Far too much time and money is wasted on the use of antibodies which don’t meet expectations. This new capability, coupled with our internal validation initiatives, is a meaningful step forward.”
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