Taking Steve Yegge's Talk to Heart: Learning Bioinformatics(thomasharrigan.com)
thomasharrigan.com
Taking Steve Yegge's Talk to Heart: Learning Bioinformatics
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.
3 comments
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.
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!
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!
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.
It often bugs me when EE/CS types talk about biology. Biology is much, much harder than tech.
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.
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.
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.