tag:blogger.com,1999:blog-37148773.post4598937012215512901..comments2024-03-27T14:50:47.345-04:00Comments on <center>Sandwalk</center>: Interdisciplinary ResearchLarry Moranhttp://www.blogger.com/profile/05756598746605455848noreply@blogger.comBlogger36125tag:blogger.com,1999:blog-37148773.post-65267503946471076952015-04-04T12:05:19.675-04:002015-04-04T12:05:19.675-04:00In considering this topic, we should not forget th...In considering this topic, we should not forget that Francis Crick, co-discoverer of DNA, was a physicist by training. colnago80https://www.blogger.com/profile/02640567775340860582noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-45415050858282763872015-04-04T05:14:02.347-04:002015-04-04T05:14:02.347-04:00I think it is a miss communication of the first or...<i>I think it is a miss communication of the first order to think that computer scientists, even theoretical computer scientists, are not relevant when it comes to practical computation.</i><br /><br />I did not make that statement nor would I endorse it. But in the particular case of being told that a problem is NP, it's not always a helpful one (and in the first example my colleague had consulted with a computer scientist who had told him the problem was NP and left it at that). I've had very productive interactions with computer scientists and I've had very unproductive ones as well and my impression is that the productive ones occur when a problem is interesting to both parties.<br />Anonymoushttps://www.blogger.com/profile/04521153536420798640noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-28269512898998207762015-04-02T00:21:36.877-04:002015-04-02T00:21:36.877-04:00SG
actually your inter exchange is geology paradig...SG<br />actually your inter exchange is geology paradigms and THEN biology data.<br />I say these two disciplines are not compatible to drawing conclusions aimed at one of the disciplines.<br />its geologists that you are relying on though you don't list them.<br />Indeed geology has no place in biology research. its an absurdity if a claim of science is made in these things.<br /><br />Robert Byershttps://www.blogger.com/profile/05631863870635096770noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-79349687928943118552015-04-01T12:58:40.866-04:002015-04-01T12:58:40.866-04:00Simon: You make a valid point. If the size of the...Simon: You make a valid point. If the size of the problem you're dealing with is small enough, then the asymptotic complexity as n increases is not the essential issue. My comments made the (unstated) assumption that computer science researchers aren't typically invited to collaborate with the biologists until the size of the problem has become a constraint on practical computation. Nor did I mean to imply that proclaiming a problem NP-complete is the end of the story. Indeed, I did suggest ways to proceed. I would characterize the subsequent examples you cited as reformulating the problem into something that is computationally tractable.<br /><br />Let me use Sudoku as my example. Sudoku is NP-complete. That's not a useful insight if you play standard 9x9 Sudoku. Trivial trial-and-error search algorithms can be written to solve 9x9 Sudoku puzzles in essentially real-time. Indeed, there is a proliferation of Sudoku solvers on the internet written in every computer language imaginable, more to illustrate idiomatic differences in the programming languages than to reveal insight into the nature of Sudoku. But, what about 16x16 Sudoku with hexadecimal "digits," 25x25 Sudoku with "digits" drawn from an alphabet of 25 characters, nxn Sudoku for arbitrarily large n? That's where the NP-completeness kicks in. But, that's not the end of the story. Suppose you had a large n Sudoku puzzle to solve (and invited me to consult). I would point you to a class of algorithms known as network consistency algorithms. Some of these algorithms are linear time. I would apply network consistency to your Sudoku puzzle. Network consistency sometimes is powerful enough to solve a particular puzzle. In general, it won't leaving you with a (reduced) problem that is still (theoretically) NP-complete. But, the reduced problem may now be computationally tractable (in practice), using the same trial-and-error search methods used for the 9x9 puzzle.<br /><br />I think it is a miss communication of the first order to think that computer scientists, even theoretical computer scientists, are not relevant when it comes to practical computation.<br />Bob Woodhamhttps://www.blogger.com/profile/04395771211353038785noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-38166226164137450502015-04-01T11:13:46.766-04:002015-04-01T11:13:46.766-04:00Is this whole "interdisciplinary" thing ...<i>Is this whole "interdisciplinary" thing just a fad? Do you know anyone whose main area of investigation spans two distinct disciplines?</i><br /><br />To some degree interdisciplinary is a buzzword attached to research and that makes it a fad. The key question is whether there are interesting research questions that require background knowledge in multiple disciplines. To some degree interdisciplinary research is marred by the fact that often there aren't and the selling point is that the research is interdisciplinary. There are additional issues which arise from how science is done in practice, which I'll discuss below.<br /><br />I know people doing interdisciplinary research. Me for a start. Deriving divergence data estimates from relaxed molecular clock models requires aligned sequences, a molecular phylogeny derived from these sequences, calibration dates taken from fossils, which require the fossils to be placed phylogenetically and assigned absolute ages.<br />Now, if you combine these you end up with the following things to do:<br />- Collecting samples<br />- Sequencing<br />- Alignment<br />- Molecular phylogeny<br />- Morphological phylogeny to assign fossils<br />- Searching for relevant fossils<br />- Assigning ages to the fossils and placing them in the phylogeny<br />- Running the relaxed clock model<br />This process requires morphologists, molecular biologists, biomathematicians and paleontologists for the various steps. My advisory board has one of each...<br />For quite some time the review process for this was inadequate, because journals would not look for reviewers covering all disciplines. For a review on how bad this can go, there's the excellent Graur and Martin paper (2004, "Reading the entrails of chickens: molecular timescales of evolution and the illusion of precision", Trends in Genetics, 20:80:86). An interdisciplinary paper should be reviewed by reviewers from all relevant disciplines. If there are methods taken from some discipline, these need to be checked by people who understand them...<br />My main focus is on taking paleontological data that so far has not been used to inform this process to improve it. I.e. I'm looking at molecular questions and look far ways that fossil data can be employed to get better answers.<br />Anonymoushttps://www.blogger.com/profile/04521153536420798640noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-11679924486979249492015-04-01T10:25:30.300-04:002015-04-01T10:25:30.300-04:00"If a computer scientist tells you that a pro..."If a computer scientist tells you that a problem is NP-complete then that computer scientist has told you something very valuable. Namely, the problem as formulated is computationally intractable. There are two ways to proceed: 1) look for approximate solutions or 2) reformulate the problem into something that is computationally tractable."<br /><br />The thing is that finding a problem to be NP hard tells you nothing about practical computation. Last week I was talking to a molecular biologist. He had a problem that was O(2^n) and considered not bothering with the problem, because it was obviously NP hard. But for his his datasets the maximum value of n was 20. It took us about half an hour to write a program that went trough an n=8 dataset in less than a second, which of course means that the n=20 case would take 2 hours at worst. Sure it won't help if n=80, but that's not a practical problem he was facing.<br />Another example comes from a recent paper of mine. The general problem is O(2^m), but for n<24 or so it works in practice. For some special cases it is possible to split up your data and get an O(2^n) and an O(2^(m-n)) problem. You can sometimes do this multiple times. Doing this makes each calculation slower by factor of about 1000, but when you go from m=60 to 3 n=20 runs, you have a massive tempo gain. This doesn't work on all possible datasets, but it always works for the special case the method was first developed for and while we're looking at somewhat more general datasets, we haven't come across one where we couldn't do it. <br />Anonymoushttps://www.blogger.com/profile/04521153536420798640noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-45469957155790020532015-03-31T00:43:47.206-04:002015-03-31T00:43:47.206-04:00I understand they think cross breeding enhances th...I understand they think cross breeding enhances the herd. They think that learning other peoples studies will sharpen one up with new imagination etc.<br />It might. <br />Largely not I think.<br />Its all about affecting intelligence curves.<br />Robert Byershttps://www.blogger.com/profile/05631863870635096770noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-25229852005673138692015-03-30T22:05:03.341-04:002015-03-30T22:05:03.341-04:00@John, yes. Mathematicians tend to want to solve p...@John, yes. Mathematicians tend to want to solve problems that are mathematically interesting instead of biologically interesting --- if they are at heart mathematicians. If they are at heart problem solvers they reach out into biology to better understand the problem(s) and mathematics is just a tools in their belt. These are sadly rare.<br />Regarding comp.sci, I think there's a dichotomy of problem solvers and controllers. The controllers restrict things as much as possible, have a love of logical purity. The problem solvers are cowboys. The best are special hybrids. roger shrubberhttps://www.blogger.com/profile/06920052094289132399noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-68996271478920003622015-03-30T19:41:20.972-04:002015-03-30T19:41:20.972-04:00OK, I can do that. In my experience, the problem w...OK, I can do that. In my experience, the problem with comp sic people working with scientists is that they want to solve a cool problem, but the cool problem turns out not to be the problem you needed to solve for biological research, because that problem wasn't cool enough. The people who have helped out my particular field through computer science have been almost exclusively biologists, e.g. Dave Swofford, John Huelsenbeck, and, dare I say it, Joe Felsenstein.John Harshmanhttps://www.blogger.com/profile/06705501480675917237noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-31572150201703270042015-03-30T19:36:55.643-04:002015-03-30T19:36:55.643-04:00In my experience, the problem with comp sci people...In my experience, the problem with comp sci people working with biologists is that many want to solve comp sci problems with "spherical cows", and the same with mathematicians, statisticians and physicists. Some 10% are exceptions. The exceptions are actual problem solvers instead of comp scientists, mathematicians, etc. They put the effort into understanding the problems which are seldom as simple as initially related by biologists. <br />We used to talk about bridging scientists. Sometimes I think they are translators: people capable of speaking biology and math, or biology and algorithms or such. <br />I've seen many examples of failed collaborations. It's easy to blame the mathematicians, comp. scientists, statisticians for their failure to learn enough to understand the actual problem that requires understanding (and they are sometimes to blame) but I have often seen biologists incapable of expressing the problem of interest in an intelligible way. There is sadly a great deal of incompetence to go around.roger shrubberhttps://www.blogger.com/profile/06920052094289132399noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-40300177203498844492015-03-30T17:52:22.993-04:002015-03-30T17:52:22.993-04:00Fascinating. Some consider biochemistry interdisci...Fascinating. Some consider biochemistry interdisciplinary as you need to understand chemistry well, much better than many biologists, and you need to understand biology as well. I would add you need to understand kinetics, which is really in addition to what many chemists or biologists learn. Personally, I happened to pick up a good deal of physics, information theory, and computer science because of the problems I was working on. The computer bit came easy to me. I tried to teach it to people who are smarter than me but they didn't think the right way and can't write an algorithm worth running. <br />Interdisciplinary is important. For example, you might begin with a biological question, have an experimental tool rooted in physics, that involves some chemistry and a signals that wind up in data that needs interpretation. Really understanding the physics of a detector helps you identify "peaks" from a mass spectrometer, understanding gas-phase ion chemistry and the physics of the machine helps understand how to make a better machine and to optimally tune the one you have. And puzzlingly through the data requires understanding all the types and sources of noise. And only knowing all that allows one to build interpretive algorithms worth a damn. It's quite interdisciplinary. You don't always have to be expert at all of it, but skilled enough to converse effectively with experts, especially being able to ask the right questions. <br />One thing that happens when you take the interdisciplinary route is that you get a great deal of grief from some who think that doing so is a bad thing. Maybe that's from seeing the approach fail so badly at times. There's some horrible bioinformatics out there, including where both the biology and the computer science is poor. Just mixing multiple disciplines isn't enough, you have to do it well.<br />One more example, the breakthroughs in looking at single neurons firing required that biologists become experts in electronics and data processing. roger shrubberhttps://www.blogger.com/profile/06920052094289132399noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-36579043575497031012015-03-30T17:39:18.332-04:002015-03-30T17:39:18.332-04:00Joe: Sorry, my attempt to embed a URL seems not t...Joe: Sorry, my attempt to embed a URL seems not to have worked. Here it is as raw text:<br /><br />https://www.cs.washington.edu/research/mlBob Woodhamhttps://www.blogger.com/profile/04395771211353038785noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-11380510887282215712015-03-30T17:38:47.232-04:002015-03-30T17:38:47.232-04:00Well, over time interdisciplinary programs do beco...Well, over time interdisciplinary programs do become mainstream. Molecular biology was originally an interdisciplinary program with elements from physics, chemistry and genetics, but eventually it was expected that all biologists (and not just those in "molecular biology" departments) could handle them. But it wasn't that way even as late as the 1980s, where "naturalists" even proudly declared their ignorance of molecular methods.<br /><br />But, say, biostatistics is decades from really being integrated into biology proper. It isn't just the ability to do a t-test in Excel or what not -- it's the ability to understand how experiments need to be designed (and with how many samples) in order to get a meaningful result. This really is a interdisplinary mixture of statistics and biology<br /><br />No, the meaning of bioinformatican hasn't changed, but yes, you can find people with very limited computational or biological skills calling themselves bioinformaticans because the demand for bioformatics is large.<br />Jonathan Badgerhttps://www.blogger.com/profile/04921990886076027719noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-11441163401413377172015-03-30T17:37:49.611-04:002015-03-30T17:37:49.611-04:00Joe: Tompa, Ruzzo and Karp are great representati...Joe: Tompa, Ruzzo and Karp are great representatives of my discipline. You identify them as algorithmists who know that statistics is important. I just did a quick check of UW's CSE web site on the machine learning group<br /><br /><a href="https://www.cs.washington.edu/research/ml" rel="nofollow"></a><br /><br />"Computational Biology" is listed as one of the group's interests. Of the 11 "core faculty" listed under "people," 3 include some flavour of computational biology as among their interests.<br /><br />It might be worth checking out what these people are doing to see if collaboration possibilities with CS have improved (from your perspective).Bob Woodhamhttps://www.blogger.com/profile/04395771211353038785noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-23055791643561907872015-03-30T16:53:16.316-04:002015-03-30T16:53:16.316-04:00Jonathan Badger,
Surely interdisciplinary science...Jonathan Badger,<br /><br />Surely interdisciplinary science cannot just mean a biologist adopting a new tool? Hardly any science can work without statistics, so <i>any</i> science would be interdisciplinary, and then the word is empty. (If everybody is special, nobody is, etc.)<br /><br />Bioinformatics is a funny one, by the way. At my alma mater I got the impression that a bioinformatician is somebody who does computer science (programming, preferably in several languages) and at the same time understands enough biology to put their programming skills to use to solve biological problems. But at my current institution I have run into people who call themselves bioinformaticians because they know how to use one (1) statistical software package and have no knowledge of biology whatsoever. Is that a local thing or has the meaning of the term changed since I was at uni?Alex SLhttps://www.blogger.com/profile/00801894164903608204noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-64753819984479603672015-03-30T16:46:52.790-04:002015-03-30T16:46:52.790-04:00Bob Woodham: I have met Ed Lazowska, but more oft...Bob Woodham: I have met Ed Lazowska, but more often communicate with Martin Tompa and Larry Ruzzo, from Ed's department. They are computer scientists (algorithmists) who have learned a <i>lot</i> of biology and know that statistics is important. They got started about the time that Richard Karp was lured to Seattle for a few years by promises (that could not in the end be redeemed). He was and is interested in biology, and he used to lecture the computer scientists on how important statistics was. And since this was Richard Karp Himself, they sat up and listened.<br /><br />And yes, knowing a problem is NP-nasty is important. But having the computer scientist act as if that is the end of the story is unhelpful. Some of them did act that way, at least in the 1980s and 1990s. I think that they have learned better now.Joe Felsensteinhttps://www.blogger.com/profile/06359126552631140000noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-66749434117356852532015-03-30T16:37:04.268-04:002015-03-30T16:37:04.268-04:00Mathematicians and physicists were particularly pr...Mathematicians and physicists were particularly prone to make another mistake. They had mathematical skills far beyond those of most biologists. So they would waltz into biology, expecting to clean up by applying standard techniques from their field.<br /><br />And every time they tried to do this, they would discover that the method they proposed was already known. Because theoretically-inclined biologists had already been ransacking the mathematical literature, desperately looking for useful techniques.<br /><br />Branch-and-bound? Oh yes, that was used by Hendy and Penny, 1982. Hidden Markov Models in molecular biology? Gary Churchill, 1989. Belief propagation on Bayesian networks? That is equivalent to what statistical geneticists discovered in 1970 and called "peeling", and I think those phylogeny folks did some of this as well. EM algorithm? Wasn't that "pre-invented" by the statistical geneticist Cedric Smith in 1954 and called "gene counting" (and reviewed by him rather broadly in 1957)?<br /><br />Invading mathematicians and physicists have had a few successes, but far fewer than they expected.Joe Felsensteinhttps://www.blogger.com/profile/06359126552631140000noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-87517749369347823032015-03-30T16:33:53.202-04:002015-03-30T16:33:53.202-04:00It's not my intent to get into a turf war here...It's not my intent to get into a turf war here. My reply is motivated, in part, by my respect for what UW has accomplished over the years in the area of computational biology.<br /><br />If a computer scientist tells you that a problem is NP-complete then that computer scientist has told you something very valuable. Namely, the problem as formulated is computationally intractable. There are two ways to proceed: 1) look for approximate solutions or 2) reformulate the problem into something that is computationally tractable. Failing that, it is appropriate for the (theoretical) computer scientist to walk away.<br /><br />There is a communications problem.<br /><br />On the computational side, there are computer scientists, mathematicians, probabilists and statisticians, each with their own sub-fields, interests and overlaps. These days, the term "machine learning" is an umbrella term for what I would otherwise call computational statistics. Machine learning researchers can be found in multiple academic departments, not the least of which is computer science.<br /><br />If you're interested (and have not already done so) talk with Ed Lazowska at UW. He's a computer systems guy, but a strong proponent of the notion that computer science is no longer a discipline that can assume error free behaviour.<br />Bob Woodhamhttps://www.blogger.com/profile/04395771211353038785noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-65436155947468959392015-03-30T15:58:44.567-04:002015-03-30T15:58:44.567-04:00I've written a few programs in my time but I d...I've written a few programs in my time but I don't pretend to be knowledgeable about algorithm theory. I don't know anything about biostatistics. In spite of these major decifiencies in my interdisciplinary education, I was able to make sense of the ENCODE data. <br /><br />Unfortunately, the "sense" that I made was very different form that of the experts in algorithms and biostatistics. Isn't that strange?Larry Moranhttps://www.blogger.com/profile/05756598746605455848noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-58266868806815507732015-03-30T12:39:28.240-04:002015-03-30T12:39:28.240-04:00It's hard to collaborate without learning anyt...It's hard to collaborate without learning anything about the other field. Often the senior people provide their expertise (old dogs and all) while the graduate students doing the work gain expertise in multiple areas, and take courses from multiple departments. If something is important and has depth, an interdisciplinary area can become it's own sub-field. Ocean biogeochemistry is an example that developed out of the need to understand the ocean carbon cycle. jbhttps://www.blogger.com/profile/10835283301887184369noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-37033812010183390172015-03-30T12:18:46.885-04:002015-03-30T12:18:46.885-04:00This comment has been removed by the author.Donald Forsdykehttps://www.blogger.com/profile/18038104286639798795noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-12710484640887547132015-03-30T12:10:45.763-04:002015-03-30T12:10:45.763-04:00Crossing boundaries sometimes fosters,
Escape from...Crossing boundaries sometimes fosters,<br />Escape from assumed paternosters.<br />New ways of thought on information<br />That squeezes through each generation.<br />Blinkered biochemists come to see,<br />Little room for redundancy.<br />Give 'em time, they'll come to say,<br />Is there junk in DNA?Donald Forsdykehttps://www.blogger.com/profile/18038104286639798795noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-24291290585879582772015-03-30T11:54:40.918-04:002015-03-30T11:54:40.918-04:00Toronto’s loss then. The rest of the world underst...Toronto’s loss then. The rest of the world understands that real bioinformatics (involving algorithmic design and not just running BLAST and what not) and the related field of biostatistics is the key to making sense of the big data we are facing these days.Jonathan Badgerhttps://www.blogger.com/profile/04921990886076027719noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-16361155701775109792015-03-30T11:40:52.629-04:002015-03-30T11:40:52.629-04:00"The Bioinformatics and Computational Biology...<i>"The Bioinformatics and Computational Biology Specialist Program</i><br /><br />There are three or less students in each year of that program and all are computer science majors who want to learn some biology so they can become bioinformaticians. They do poorly in the biochemistry courses so many of them drop out. <br /><br />You must be really stupid to think that I don't know what courses university undergraduates take and what goes on in my own department. I taught bioinformatics for several years but what students need to know about bioinformatics is trivial compared to the discipline of computer science. <br /><br />We tried to set up a bioinformatics program in the 1990s but we decided that the cultures were so different that students could not become competent in either discipline. Some of my colleagues gave it a go in 2004. Turns out I was right. <br />Larry Moranhttps://www.blogger.com/profile/05756598746605455848noreply@blogger.comtag:blogger.com,1999:blog-37148773.post-71088856736729623252015-03-30T11:25:19.585-04:002015-03-30T11:25:19.585-04:00Yes, there was certainly a lot of biological naive...Yes, there was certainly a lot of biological naivety among computer scientists tying to get into computational biology in the late 1990s-early-2000s when I was a postdoc. <br /><br />There would be computer scientists who thought sequence assembly was a trivial problem because it was just a matter of finding the shortest common superstring, with no understanding that sequencing error made that impossible. Statisticians understand that data has errors.<br />Jonathan Badgerhttps://www.blogger.com/profile/04921990886076027719noreply@blogger.com