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From: Robert Goldwein (robert.goldwein_at_[hidden])
Date: 2006-12-06 22:02:55

Well, I'm sorry for this confusion, you're naturally right. The problem
is that any bioinformatics textbook (e.g., basic, but one of the very
best is introduces these algorithms, so
as time goes by, my perspective becomes somewhat limited ;-) The best
way would really be to extend appropriate libraries (string & math
algorithms), and create that bionf library with really bioinf-specific

Thank you all for your responses in this matter, this is the help I was
hoping for.


-----Original Message-----
From: boost-bounces_at_[hidden]
[mailto:boost-bounces_at_[hidden]] On Behalf Of Beman Dawes
Sent: Tuesday, December 05, 2006 19:52
To: boost_at_[hidden]
Subject: Re: [boost] Bioinformatics algorithms in boost?

Robert Goldwein wrote:
> Hello all,
> for my thesis, I'll be developing a self-contained framework for
> algorithms used in bioinformatics. This will include algorithms such
> Hamming distance, Levenshtein distance or Longest common subsequence
> algorithms, gene prediction algorithms, 2D and 3D scoring matrices,
> alignment problems, etc.
> Would be there - in some near future - any interest in such library?

Yes, as others have also indicated.

The real question is why you think of these as "bioinformatics
algorithms" rather than just plain "algorithms"? Have they been
restricted in some way that prevents them from being used for general

I've used Levenshtein distance variants a great deal in geographic name
processing applications. For real-world applications, there has to be a
way to recognize additional distances (i.e. costs) in various cases. I
expect the same refinements apply to many problem domains. Wouldn't the
same apply to the bioinformatics domain?


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