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(inactive and unmaintained) A set of loosely coupled machine learning, data-mining and bioinformatics applications in a broad range of functional languages.

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Octocat-spinner-32 SequenceML
Octocat-spinner-32 bioclojure
Octocat-spinner-32 bioscripts
Octocat-spinner-32 clj_pangool
Octocat-spinner-32 cljcrawl
Octocat-spinner-32 dojo
Octocat-spinner-32 javamltb2
Octocat-spinner-32 py_tld
Octocat-spinner-32 py_vw
Octocat-spinner-32 ss_haskell
Octocat-spinner-32 .gitignore
Octocat-spinner-32 README.md
Octocat-spinner-32 RudolF.pdf
README.md

Welcome to RudolF!

This project is named after the Reindeer, whose red nose was an asset to Santa on Christmas. This analogy is meant to reflect the notion that functional programming and dynamic languages CAN have a very meaningful role in the "real world" --- and project RudolF aims to realize this notion. The aim of RudolF is to exemplify the power of functional and dynamic languages applied to a broad range of data mining problems.

BioClojure:

This project demonstrates the ease of java-interop for sophisticated bioinformatics data integration and processing.

BioClojure integrates jMol, BioJava, and a few algorithms/methods for NMR data normalization which allow for visualization and 3D analysis of proteins and their structures.

This is a very interesting project for anyone in the bioinformatics community who wants to get involved with functional programming.

Thanks to Lee Hinnman for helping us get this off the ground!

Contributors are Jay Vyas, Matt Fenwick and Colbert Sesanker.

Haskell:

This is a general source code repo for haskell data processing.

Right now, it supports the computation of random sample schedules for collecting distributed points at different times in non-uniform NMR experiments. There will be more to come!

Currently, we are reaching out to people in the Erlang, Javascript, Scala, and OCaml communities for more contributions.
The end goal of RudolF is to demonstrate, in Sandbox fashion, a broad range of functional and dynamic programming paradigms applied to real world problems in data mining and analysis.

But of course, if you want to start out with a 'hello world' just to get involved, you're MORE THAN WELCOME to join us!

SequenceML

This is a repo for machine learning algorithms applied to sequence (peptide/nucleotide) data.

There is currently a muti-classification algorithm, the AminoAcidPredictor which tries to understand local features in proteins. It is described in detail in the SequenceML repo.

contact:

  • jayunit100 at gmail dot com
  • mattfenwick100 at gmail dot com
  • sesanker0 at gmail dot com
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