Open course ware

PETER PARK: For today, I'll just talk a little bit more generally at the beginning about a few observations that I've had. Perhaps a little bit about reliability microarray studies. I'll talk about classification problem in general. And then, I'll talk more about phenotypes. And then review some literature that are well cited. So I bet you've had some exposure to that, right? Did anyone-- maybe Marco, or Zack, talk about lymphoma studies? Do you remember any other papers that you've covered? Yeah? AUDIENCE: [INAUDIBLE] PETER PARK: Anything else you remember? All right, so-- AUDIENCE: [INAUDIBLE] PETER PARK: Yeah, right, so maybe this stuff that I have is new. OK, good. So you probably know all this. It's actually-- so the cDNA arrays are cheaper. So they are used a lot of biology labs. The ones that I work with, for example, in the new building, they have their own libraries. They print their own. Affy arrays are-- I think the consensus that th...

PROFESSOR: OK. Today I'll use this trinity-- the science, engineering, technology trinity-- as sort of a theme that we're going to develop through the whole term. And we'll just briefly refresh ourselves, or remind ourselves, what Professor Lauffenburger talked about on Tuesday. And then I'm going to go a little bit into some detail about how information is used in biology, in cells. It's a way of gaining a broad perspective of a facet of the biological basis for bioengineering. but it's also a way to introduce to you how we can use that information in developing technology. OK. Now just a few odds and ends. We do have a course website. On that course website will be articles that I may or may not download from newspapers or magazines. Generally, if you want to be in the biospace, read the newspapers, you know. Today the concern is the occurrence of the flu virus in Africa now. So it's now spread from China to Turkey from the Middle East down t...

PROFESSOR: I'll begin by reviewing quickly what we did last time. We considered what are called finite range potentials, in which over a distance R, in the x-axis, there's a non-zero potential. So the potential is some v of x for x between capital R and 0, is equal to 0 for x larger than capital R, and it's infinity for x negative. So there's a wall at x equals 0. And there can be some potential, but this is called a finite range potential, because nothing happens after distance R. As usual, we considered scattering solutions, solutions that are unnormalizable with energies, h squared k squared over 2m, for a particle with mass m. And if we had no potential, we wrote the solution phi of x, the wave function, which was sine of kx. And we also wrote it as a superposition of an incoming wave. Now, an incoming wave in this set up is a wave that propagates from plus infinity towards 0. And a reflected wave is a wave that bounces back and propagates towards more...

DAVID SONTAG: So I'll begin today's lecture by giving a brief recap of risk stratification. We didn't get to finish talking survival modeling on Thursday, and so I'll go a little bit more into that, and I'll answer some of the questions that arose during our discussions and on Piazza since. And then the vast majority of today's lecture we'll be talking about a new topic-- in particular, physiological time series modeling. I'll give two examples of physiological time series modeling-- the first one coming from monitoring patients in intensive care units, and the second one asking a very different type of question-- that of diagnosing patients' heart conditions using EKGs. And both of these correspond to readings that you had for today's lecture, and we'll go into much more depth in these-- of those papers today, and I'll provide much more color around them. So just to briefly remind you where we were on Thursday, we talked ab...