I was thrilled that several PLoS colleagues attended the ISMB conference this past week in Toronto, where I live. The massive event is the official conference of the International Society for Computational Biology, and PLoS Computational Biology is the official journal of the Society.
My friends asked me: “What is computational biology?”
I said: “Good question!”
Wikipedia tells me it’s an intoxicating mix of computer science, applied mathematics, and statistics applied to biology. (ok, I added the intoxicating bit).
My PLoS Biology colleague Robert Shields tells me it’s “the biology that counts.” (ba dum dum).
Definitions aside, I took two things away from the conference that matter to PLoS Medicine.
First, Mark Gerstein from Yale University gave an outstanding talk in a session called The Future of Scientific Publication, remarking that it was unusual (but inspiring) for a computational biology conference to include a session on publishing.
He emphasised the use of text mining to study the “structure of science.” He says this is Science 2.0 (or, the science of science), which uses network theory and technologies to discover new scientific relationships. Whereas conventional challenges have us struggling to keep up with the volume and growth of scientific papers (this reminds me of Muir Gray’s information paradox in medicine – overwhelmed with information but unable to find the knowledge when we need it), new technologies to structure and text mine scientific publications can help scientists share information and foster collaboration. (Including using publications as the annotation for the genome).
Gerstein and colleagues’ fascinating maps of publication patterns, gene names, topic clusters within whole research areas, and the emergence of new scientific fields are reminiscent of the recent PLoS Medicine editorial in which we argue that everything in medicine is connected through networks. (When I chatted with Gerstein after his session he agreed that network guru Albert-Lásló Barabási is a genius.)
But none of this is possible without open access, countered Matt Cockerill from BioMed Central. He said that we absolutely need the raw material (whether it be biological data or bibliometric information) freely and openly available to apply the network algorithms so we can visualise the structure of science. Currently, much information is behind access controls thus disrupting the whole vision of an interconnected and collaborative scientific world.
The second issue of note was raised during the session’s Publishers’ Panel, populated by Catherine Nancarrow (PLoS), Claire Bird (Oxford University Press), and Matt Cockerill (BioMed Central). Panellists noted that the recent NIH public access policy emphasises free not open access. That is, the policy may lead to freely accessible publications (for which publishers or organisations may reap profits from charging authors a fee to deposit their manuscripts), but these will remain under restrictive licenses (thus limiting text-mining).
This, Cockerill argued, makes the NIH policy regressive.