life science research

Will Artificial Intelligence (AI) Transform the Future of Life Science Research?

Posted on by Ken Doyle

Artificial intelligence (AI) is not a new technological development. The idea of intelligent machines has been popular for several centuries. The term “artificial intelligence” was coined by John McCarthy for a workshop at Dartmouth College in 1955 (1), and this workshop is considered the birthplace of AI research. Modern AI owes much of its existence to an earlier paper by Alan Turing (2), in which he proposed the famous Turing Test to determine whether a machine could exhibit intelligent behavior equivalent to—or indistinguishable from—that of a human.

The explosive growth in all things AI over the past few years has evoked strong reactions from the general public. At one end of the spectrum, some people fear AI and refuse to use it—even though they may have unwittingly been using a form of AI in their work for years. At the other extreme, advocates embrace all aspects of AI, regardless of potential ethical implications. Finding a middle ground is not always easy, but it’s the best path forward to take advantage of the improvements in efficiency that AI can bring, while still being cautious about widespread adoption. It’s worth noting that AI is a broad, general term that covers a wide range of technologies (see sidebar).

AI personified looking at a dna double helix against an abstract cosmic background
Image generated with Adobe Firefly v.2.

For life science researchers, AI has the potential to address many common challenges; a previous post on this blog discussed how AI can help develop a research proposal. AI can help with everyday tasks like literature searches, lab notebook management, and data analysis. It is already making strides on a larger scale in applications for lab automation, drug discovery and personalized medicine (reviewed in 3–5). Significant medical breakthroughs have resulted from AI-powered research, such as the discovery of novel antibiotic classes (6) and assessment of atherosclerotic plaques (7). A few examples of AI-driven tools and platforms covering various aspects of life science research are listed here.

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Collaboration Brings Researchers to a New Level of Discovery

Posted on by Melanie Preston
2018 Steenbock Symposium program graphic. Source: University of Wisconsin-Madison Biochemistry Media Lab

At the 2018 40th Steenbock Symposium at University of Wisconsin-Madison, twenty-seven researchers from RNA and related fields convened at the Wisconsin Institute for Discovery to share “eureka” moments in their careers. It was so inspiring to hear from founding members of the RNA community, including Joan Steitz, Christine Guthrie, John Abelson, and Harry Noller. I noticed a recurring theme throughout the talks: many of these epiphanies resulted from informal meetings (quite often at a bar or social event) between colleagues in different groups, sometimes from different universities. They discussed tough problems and brainstormed about how to solve them, pondered about what their peculiar results could mean biologically, or dreamed, “wouldn’t it be cool if we could  <insert awesome idea here>?” and then came up with a way to do it. It sounded like a wonderful time to be a scientist! Sitting together freely sharing ideas, motivated by curiosity and the joy of doing science.

As I thought back to my research career to look for instances of such encounters, I was happy to find a few. “Philosophy” Meetings during grad school and Tea Time during my postdoc—informal social events to bring people together from different labs and departments with drinks and snacks. RNA Cluster Meetings during grad school and RNA MaxiGroup during my postdoc—events where people interested in a certain research area (in this case RNA) would gather for dinner and to hear an informal research talk. These organized events were intended to provide a forum for conversations between scientists to spark new ideas. Sometimes, I would talk to someone in a totally different field and learn something new. But I really didn’t have an epiphany about my own research. I often found myself (and others) scurrying away after the event to get back to lab work. Was I missing out on the best part of the meeting: the after-discussion?

My reflection on the Steenbock Symposium talks led me to ask a somewhat troubling question:

In today’s competitive research environment, have we missed out on crucial discoveries and technological advances because they weren’t given the right environment in which to develop?

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