PCP-ML: Protein characterization package for Machine Learning

Jesse Eickholt, Zheng Wang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Background: Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. To speed further development of machine learning based tools and their release to the community, we have developed a package which characterizes several aspects of a protein commonly used for protein prediction tasks with machine learning. Findings: A number of software libraries and modules exist for handling protein related data. The package we present in this work, PCP-ML, is unique in its small footprint and emphasis on machine learning. Its primary focus is on characterizing various aspects of a protein through sets of numerical data. The generated data can then be used with machine learning tools and/or techniques. PCP-ML is very flexible in how the generated data is formatted and as a result is compatible with a variety of existing machine learning packages. Given its small size, it can be directly packaged and distributed with community developed tools for protein prediction tasks. Conclusions: Source code and example programs are available under a BSD license at http://mlid.cps.cmich.edu/eickh1jl/tools/PCPML/. The package is implemented in C++ and accessible as a Python module.

Original languageEnglish (US)
Article number810
JournalBMC Research Notes
Volume7
Issue number1
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Keywords

  • Machine learning
  • Protein characterization
  • Protein software package
  • Protein structure prediction

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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