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[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]Re: [tlug] Open Access Journals
- Date: Fri, 4 Apr 2014 12:07:50 +1100
- From: Jim Breen <jimbreen@example.com>
- Subject: Re: [tlug] Open Access Journals
- References: <53292BF2.6030309@dcook.org> <CAAhy3dsA3yJ+dhP8y5AnkDm0Rhepfe6TyxXwENkiWtrqtqAgYQ@mail.gmail.com> <20140322100123.920638c262ed2e35be0ecc2d@kinali.ch> <87zjkggv3n.fsf@uwakimon.sk.tsukuba.ac.jp> <20140326092128.ce15a21d03bfafbbcfd660d5@kinali.ch> <87wqfgown8.fsf@uwakimon.sk.tsukuba.ac.jp> <87ppl7ou5g.fsf@uwakimon.sk.tsukuba.ac.jp> <20140330123127.db17cd41959005fa6002d3c6@kinali.ch> <8761mvoe5x.fsf@uwakimon.sk.tsukuba.ac.jp> <CAAhy3ds-Tfno8KrsexFnB3CracrTYUpM962HD4jETsVaVAKxhA@mail.gmail.com> <8738hznmc9.fsf@uwakimon.sk.tsukuba.ac.jp> <CAAhy3dv+rG14dht0naZCdnoR=ADGgEStOCxd8awa+AmtCwB=qw@mail.gmail.com> <533DFA42.5030206@dcook.org>
On Apr 4, 2014 11:25 AM, "Darren Cook" <darren@example.com> wrote:> > Now, it's almost reached a point in bioinformatics where we don't know
> > what we're looking for...so let's throw it into a piece of software
> > (i.e., a machine learning algorithm for example) -- it tells us which
> > genes out of the thousands that were examined are interesting in a
> > statistically significant way, and then we write a paper about it.
>
> There does seem to be a paradigm shift in the way science is done.There have always been many models of "the way science is done".
Consider a botanist working in a previously unexplored area, and a
medical researcher evaluating a newly identified blood agent as an
indicator for a type of cancer, and cosmologist busily working on the
ripples in space-time in the nanoseconds after the big bang. All
scientists, all working in totally different paradigms.I relate to the quote above because my own research involves using
AI techniques to detect useful patterns in large amounts of text. It's
a fairly foreign concept to people brought up on traditional views of
the "scientific method", and I confess to a little irritation with some
practitioners in NLP who chew over a lot of very noisy data and then
come to conclusions based on results that aren't within a bull's roar
of being statistically significant (I majored in stats way back in the
60s.) Still "social scientists" have been getting away with that for
decades.Jim
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