Thinking About Bioinformatics

In June 2005 I wrote out some ideas on improving interdisciplinary peer review, in the form of a mock proposal for creating an interdisciplinary journal (referred to as “New Biologist” in the text below). I later condensed this into a very brief piece in Nature Online’s peer review focus.  Recently, Russ Altman asked me to post the longer, detailed proposal so he could refer to it on his blog.  Here it is.
Goals
New Biologist will create a home for high-impact interdisciplinary research in the biological sciences, which is ill-served by existing publishing and peer review models.  It will do so by focusing on solving the problems of interdisciplinary peer review, and of targeting (and in some cases creating) interdisciplinary audiences.  We will provide a new channel for creative, path-breaking work that crosses boundaries and does not easily fit any single discipline.  We will pioneer new models of interdisciplinary peer review, and the creation of “virtual journals” that can serve as community centers for new audiences forming at the intersection of multiple disciplines.  For all of these goals, we will make use of new technologies for rapid interaction and community building based on the Internet.

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As I discussed in a previous post, I want to emulate the admirable clarity and accessibility of Feynman’s Lectures on Physics in my own attempt to write an introductory textbook on information metrics for statistical inference.  Below are my thoughts on how I can apply the lessons that I drew from Feynman in my previous post.

More to the point, I’ve rewritten Chapter 1  What is Inference? based on these lessons.  So now I ask you: is this a genuine improvement?  Note that this is an intro chapter with only the simplest math (some addition and multiplication), so anyone should be able to understand it and critique it!  Please add comments to this post to give your opinion of whether you think the specific changes I outline below improve the chapter, compared with the original version.  I am particularly interested in both whether you think the ideas in my plan are the right direction to pursue, versus whether their actual “reduction to practice” in the new draft chapter works or not.  Above all, tell me how I need to improve my chapter and my writing!

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I just refactored a number of internal aspects of Pygr’s object-relational model, to make use of a new pattern I’m calling “subclass binding”, which I’ll try to explain a bit in this post.  First I’ll try to explain the problem from the viewpoint of a Python programmer.

Object-relational design makes modularity even more difficult than usual. It illustrates a general problem: when you try to combine two different behaviors (e.g. a local Python object and a back-end database) into one object, all sorts of confusion can ensue.

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 I’ve been thinking about how to make my draft textbook on “information metrics” more accessible.  In particular, I’ve spent some time looking at a text that I admire as very accessible — Feynman’s Lectures on Physics — to see what I could learn.  I thought I’d post some of my conclusions here.

Feynman achieves a remarkable combination of intellectual engagement — plugging you into the fundamental ideas of a problem area — and accessibility. Conventional textbook treatment uses formalism and jargon to elevate the author and distance the reader from the material. It feels like you are being inducted into the holy mysteries… which puts most people to sleep. Instead of stimulating your own questioning of the material, it implies that such Difficult and Important Ideas will require long, hard hours of study to get even a glimmer. Feynman could easily come across as “too smart for a normal human to understand”, but unlike some writers, that’s not what he wants. Somehow he is able to prick that bubble effortlessly and give you the feeling of a wonderful tour guide who is going to show you his favorite marvels. No barriers of jargon or “obfuscation-sophistication” get in the way of understanding him. He simply refuses the conventional academic tone. He doesn’t believe in it!

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This week-long workshop was a highlight of my time at IMA. I really enjoyed the diversity and excitement of this new field. Here’s a video of my talk on HIV drug resistance evolution.

Yesterday I talked about applications of potential information to experiment planning, using the example of a robot seeking to discover the principles of genetics from the initial observation of a “mutant” pea plant with white flowers. You can listen to the audio (right click on the audio link, and Save Link As, then listen to the downloaded file using QuickTime player or Real player). I also captured most of the material I wrote on the whiteboard.  Some relevant background material (and detailed exposition of the RoboMendel example) is also available.

Well, I failed to record my seminar audio, but here are some relevant notes for material discussed in the third seminar. This time we discussed the application of information metrics to experiment planning, rather than just model selection. One metric that I emphasized this time is the notion of potential information, which provides a signal for whether the current model needs to be expanded because its fit to the observations is inadequate. The attached material discusses some concrete examples of potential information, for example, for experiment planning.

I gave a talk today on my lab’s work on HIV drug resistance evolution and conditional selection pressure “networks”. The slides and audio are available here (to download the audio right-click the link and select Save Link As…). To listen to the audio you can use the QuickTime player or Real player. For our publications on this topic, see our publications page.

Here are my slides for my second talk at IMA on Feb. 14. I tried to introduce some problems with typical information metrics as they apply to statistical inference problems. Then I describe empirical information, my preferred information metric for statistical inference. The slides are available as a PDF, and the audio of the talk is also available — you can use either RealPlayer or the Quicktime player to listen to this. To download the audio, right-click on this link and choose Save Link As…

I’ve also posted some background material cut from different chapters of my draft textbook as a PDF.

Here are a couple of items relevant to my Feb. 7 intro session at IMA:

• Chapter 1 draft introduction to probabilistic inference.
• Paper on identification of SNPs from EST chromatogram data.