Abstract
BackgroundOne of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research.ResultsTo meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.ConclusionPROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.Electronic supplementary materialThe online version of this article (doi:10.1186/s13029-015-0047-1) contains supplementary material, which is available to authorized users.
Highlights
One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research
One of the main challenges of computational biology is developing new algorithms, tools and software to facilitate analysis of Big Data generated by biomedical research
* Correspondence: sjahandideh@sbpdiscovery.org; adam@godziklab.org 1Bioinformatics and Systems Biology Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92307, USA Full list of author information is available at the end of the article time PROPER allows semi-automated optimization of complex methods, such as Artificial Neural Network (ANN)
Summary
One of the recent challenges of computational biology is development of new algorithms, tools and software to facilitate predictive modeling of big data generated by high-throughput technologies in biomedical research. Optimization and comparison of different prediction methods, selection and evaluation of importance of different features, and simple and efficient validation of predictors’ performance is crucial for successful application of machine learning algorithms. To assist in this task, PROPER provides visual monitoring of optimization and comparison of ranking classifiers.
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