

Taking Steve Yegge's Talk to Heart: Learning Bioinformatics - tomharrigan
http://thomasharrigan.com/getting-started-with-bioinformatics/
Steve Yegge gave a great talk about why we should tackle real world problems instead of building social networks. This is the first step on that path.
======
tomharrigan
Steve Yegge gave a great talk about why we should start tackling real world
problems instead of building another social network. This is the first step.

------
fghh45sdfhr3
If you have a CS degree and you get into Bioinformatics by reading books about
it... you're dipping your toes into it.

If on the other hand you go ahead and get another degree _then_ you're taking
things to heart.

In other words, bioinformatics programming is easy for programmers. Biology is
not. If you want to be a hero, really go for it!

~~~
tomharrigan
Right on. I see your point. Part of what I'm doing is taking some of the MIT
OpenCourseWare classes, which yes, is mostly reading, but that seems to be a
lot of what getting another degree would consist of, though without the
research portion. A lot of it is that I'd really like to understand what I'm
programming, why I'm programming it and how it's benefitting the community. I
think having that knowledge allows you to look at the problems with much
better sight and see those things that you might not think of or overlook
otherwise.

------
smparkes
It often bugs me when EE/CS types talk about biology. Biology is much, much
harder than tech.

~~~
tomharrigan
I don't think I made any claims about relative difficulty, or even in general
that Biology is easy. If you look at my post, I have a ton of links to things
I think I need to learn. I even included Intro to Biology on my course list.
My viewpoint is not that tech people should should move into Bio-related
fields because it's easier, but rather because it serves greater benefit to
the world than using those skills to build 'cat picture sharing sites'. We
have differing skill sets, but if we can become passably versed in each others
fields and learn the basics, it would seem to make the challenges that need to
be/are being faced a lot easier since we'd now be speaking the same language
and can understand what needs to happen to reach those goals and why. I really
appreciate your input and if you have any suggestions on starting points, that
would be fantastic. My apologies if my post came off in a way that would
suggest I was taking the difficulties of Biology for granted.

~~~
smparkes
Actually, my comments were towards what Steve said, not what you said. Sorry
that that wasn't clear.

Steve says biotech is data mining, and it's not. It's a physics problem. A lot
of very, very hard physics problems. On systems that are hard to observe,
since observing them tends to kill them and/or otherwise change the way they
function.

Andy Grove made an even more egregious example of this a few years ago. In a
talk he gave, he spoke at length about how biotech needed to learn from tech.
Anybody that knows anything about the complexity of biology would find his
comments ... well, calling it naive would be very kind.

Most people, including tech people, don't understand how vastly more complex
biology is than tech.

It's not actually clear how tech can help biotech. There was a rush of work in
the 90s related to sequence reconstruction that has been very useful in
reducing a lot drudgery. But not necessarily much that has been able to move
higher up the stack, stuff like systems biology. These systems are so complex
and non-linear that analytical tools often get overwhelmed or don't produce
meaningful results because the models and observable data are so radically
simplified.

It's not that it shouldn't be worked on. It's just that indications of the
likelihood of a singularity/inflection point aren't so high as seem to be
often spoken of.

