PCP-ML: Protein characterization package for machine learning

BMC Research Notes

Zheng Wang¹  Jesse Eickholt² 

[1] School of Computing, University of Southern Mississippi, Hattiesburg, MS 39406, USA;Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA

关键词: Machine learning;    Protein software package;    Protein characterization;    Protein structure prediction;   

Others  :  1123247
DOI  :  10.1186/1756-0500-7-810

 received in 2014-01-27, accepted in 2014-10-31,  发布年份 2014

【 摘 要 】

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/ webcite. The package is implemented in C++ and accessible as a Python module.

【 授权许可】

   
2014 Eickholt and Wang; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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