Thinking About Bioinformatics

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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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.

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.

Does there exist an information metric with truly general utility? If so, a scientist could use it to choose which experiment to do: the best experiment is that one that yields the largest amount of information about the scientist’s question of interest (or, over the long-term, the highest information rate per unit time / expense). Indeed, if the metric were truly general, the scientist could use it to decide which research question is “most interesting” (again, compute the expected information yield for the different research directions). Actually, if such an information metric existed, the “scientist” could just be a robot, because all that is required is the ability to calculate this metric for different possible experiments (observations). This wouldn’t be artificial intelligence in the traditional sense of that field, but instead just a big statistical number-crunching computation. In a way, scientific computing at its dullest.
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