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

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.

Continue reading “Will Artificial Intelligence (AI) Transform the Future of Life Science Research?”

Finding the Next Generation of Antibiotics

Mention the word penicillin and it conjures up images of mold growing on bacterial culture plates and Dr. Alexander Fleming observing that the mold had killed the surrounding bacteria, ushering in the age of antibiotics. Bacterial infections could easily be treated with penicillin or any one of the bewildering array of new antibiotics continually being discovered. The result of using these antimicrobial drugs: numerous lives were saved and human health improved. However, bacteria are clever organisms and as quickly as humans developed an antibiotic to treat infection, the microbes would find a way around the bacteriostatic or bacteriocidal compound. It is a scary world where antibiotics are rendered impotent and fewer and fewer weapons are left in the arsenal to treat multidrug-resistant Staphylococcus aureus (MRSA) and hospital-acquired drug-resistant Gram-negative bacteria (e.g., Acinetobacter baumannii). Continue reading “Finding the Next Generation of Antibiotics”

Wolfram|Alpha: The World’s Knowledge, Computed

Friday, May 15th, 2009. 11 days ago. The 135th day of 2009, in the 20th week. 36.71% of 2009 had elapsed, and 63.29% remained.

Depending on where you keep your ears tuned on the internet, it may have passed as just another nascent weekend, or you may have been waiting for this day for quite some time – not because it was the 97th weekday of 2009, but because of the launch of Wolfram|Alpha.

I can’t claim the hot level of anticipation that others would – I’d heard the name thrown around on tech blogs and treated with a certain reverence. But starting about two weeks ago, I got feverish emails and IMs from various net.friends.

“Wolfram Alpha launches on Friday.”

“Did you hear about the launch?”

“Check out this screencast NOW.”

Anything subtitled “Computational Knowledge Engine” would normally earn ample mockery from my friends and I, but a few quick queries on the site should disarm all but the most eager attackers.

This is no mere search engine: compare this random string of letters – ACTTACAATG – and the difference in results between Google and Wolfram|Alpha.

There’s an entire section of examples of Life Sciences queries, including a Molecular Biology subsection.

This is less search than it is some kind of very fancy calculator. My hunch is that we’re only hearing the beginning of this tool.