Abstract
BackgroundMachine 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.FindingsA 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.ConclusionsSource 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.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-0500-7-810) contains supplementary material, which is available to authorized users.
Highlights
Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction
Source code and example programs are available under a BSD license at http://mlid.cps.cmich.edu/ eickh1jl/tools/PCPML/
Machine Learning (ML) techniques have been successfully applied to a variety of protein related classification tasks
Summary
Machine Learning (ML) has a number of demonstrated applications in protein prediction tasks such as protein structure prediction. Machine learning has proven quite useful in the area of protein structure prediction and resulted in the development of a number of tools and particular applications. * Correspondence: eickh1jl@cmich.edu 1Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA Full list of author information is available at the end of the article protein prediction tasks, the primary feature space is the protein’s sequence and/or data directly derived thereof (e.g., sequence profile).
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