5 Big and ‘Small’ Ways Biotech Research Will Improve in 2017

Published: Dec 20, 2016

5 Big and ‘Small’ Ways Biotech Research Will Improve in 2017 December 20, 2016
By Renee Morad, BioSpace.com Breaking News Staff

Biotech breakthroughs and advancements hinge on sound research and effective laboratory experiments. With this in mind, search engine Bioz is working to revolutionize the way scientific data is consumed—and has made five bold predictions about how industry improvements could lead the way for more efficient lab experiments in 2017.


The search engine Bioz mines hundreds of millions of pages of complex, dense and unstructured life sciences papers and —with the help of natural language processing and machine learning—provides objective recommendations, ratings and insights for hundreds of millions of reagents, lab equipment, assays, protocols and researchers.

Essentially, the resource makes it easier for researchers or companies to shift through a massive amount of published studies to quickly and efficiently determine the ideal components for their experiments.

Bioz Chief Executive Officer Daniel Levitt said that the process for researchers conducting experiments in labs is similar to the process for a chef in the kitchen, who has to request the right ingredients to prepare meals. However, when it comes time for researchers to determine which ingredients will work best for the experiments they conduct, the massive amount of published reports that they need to review can become a huge and time-consuming undertaking—often taking weeks to months just to pinpoint the right components.

Bioz.com is working to fill with void. The search engine, which currently has about 100,000 users, with weekly growth of roughly 10 percent, provides easy access to a condensed list of relevant articles, along with ratings on lab components, suggestions on the best reagents to use in an assay, optimal dilutions and temperatures, and more.

Here are five big things that Bioz expects to improve for researchers in 2017:

1. Cloud Technology Will Take Center Stage

In 2017, modern IT and cloud technology is expected to be an even larger driver in advancing research methods in life science, and biotech companies will begin to play catch up, Levitt said. “This will be driven by more investments in companies that are not only in the biology space, but also in companies that are focused on what Andreessen Horowitz dubbed as ‘cloud biology.’” Cloud biology is essentially software that is used to improve life science research and drug discovery. Levitt anticipates a strong shift towards “utilizing the power of the computer, the cloud and other current technologies.”

2. Biotech Companies Will Further Embrace e-Commerce

Traditionally, biotech companies have relied on typical sales and marketing approaches like direct sales, mailings, print advertising and trade conferences to promote their products, but there has been a recent burst of activity in biotech companies embracing e-commerce, Levitt said. According to PricewaterhouseCoopers, e-commerce sales in the life science research market will reach 80 percent by 2020.

Especially when it comes to purchasing lab ingredients, the companies that are manufacturing these products often have minimal offerings online, publishing a PDF but then requiring consumers to call to order, Levitt explained. From what he has seen so far, e-commerce will be a big area for improvement in the year to come, as more companies begin to transition to e-commerce.

3. With Greater Emphasis on Quality, Less Time and Money Will Be Wasted in the Lab

Greater transparency regarding the quality and compatibility of life sciences tools is expected to be another bright spot within the next 12 months. With wasteful experiments resulting in hundreds of millions of dollars being spent on ‘bad’ products and millions more spent on wasted researcher time, Levitt expects that the public “will demand more transparency” to know that taxpayer money that’s used towards government-funded experiments is put to good use.

4. A Need for Data Transparency Will Lead to More Accessible Research

Data transparency in general could also improve in 2017 if more journal publishers shift toward making scientific articles publically available. “In an industry focused on work as imperative as life science research, and with the decline of printed journals in favor of online information, it is unacceptable that researchers are blocked by publisher pay-walls,” Levitt said. “These pay-walls require money from researchers to allow them to view articles that they need to read to do their jobs.” On the other hand, while publishers of scientific journals have their financial reasons for requiring a pay-wall, another potential solution could be a push for big universities to provide a blanket subscription to all scientific journals, Levitt said.

5. Objective Ratings Will Replace User-Generated Reviews

Heading into 2017, Bioz’s chief scientific officer Dr. Karin Lachmi predicts that there will be a greater emphasis on data-driven objective rating parameters for lab ingredients, rather than user-generated reviews and ratings. This emphasis would minimize the risk of “fake reviews,” which are often times funded by the companies themselves and could alter the success of lab experiments by failing to allow researchers to home in on the ideal components. “Researchers don’t just want reviews of ingredients, but they also want objective data about those ingredients, along with objective data about equipment, tools, kits, and any other aspect of their experiments,” Lachmi said.

If all of these predictions come together in 2017, this could pave the way for great strides in biotech discovery.

“Each of these smaller predictions will improve the industry as a whole,” Levitt explained, adding that these gains could significantly help researchers in the labs of academic institutions and in industry. “If researchers could work more efficiently and effectively, aside from money, people will be able to work faster, shorten the time it takes to get a result and be less likely to repeat the same mistakes that other researchers already experienced.”

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