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Think about Loose Coupling

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Some define bioinformatics narrowly as computational genomics and proteomics. Others use bioinformatics more broadly as a term for computational biology in beneral.

I fall into the latter category. I am a computational neurobiologist who studies how neurons in sensory systems code information using action potentials or "spikes". The analysis is purely mathematical and draws from algorithms in signal processing, nonlinear physics, and information theory.

Perl is my first choice of language for programming in this domain. It allows me to try ideas quickly and is often fast enough for what I need. Some algorithms are CPU and/or memory intensive, in which case I'll turn to the Perl Data Language (PDL) to cut the problem down to size. PDL is the tool of choice for signal filtering, FFTs, and correlational analysis.

I occasionally write applications in C++, but it is more to keep up my skill set than desperate need. Others in my department use Perl or Python for a similar tasks. I think Python seems to attract more numerical analysts for some reason, but PDL is nicer than the melange of numerical modules one has to deal with in Python.

I have also worked in bioinformatics in the narrow sense. I used Perl to attack the protein folding problem using a genetic algorithm Monte Carlo technique. I am interested in doing more work in genomics and proteomics. There are a lot of fascinating problems in those domains.


In reply to Re: Other Bioinformatics Monks Out There? by kvale
in thread Other Bioinformatics Monks Out There? by BioGeek

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