- Open Access
PROPER: Performance visualization for optimizing and comparing ranking classifiers in MATLAB
© Jahandideh et al. 2015
- Received: 13 July 2015
- Accepted: 30 November 2015
- Published: 3 December 2015
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.
To meet these demands we developed PROPER - a package for visual evaluation of ranking classifiers for biological big data mining studies in the MATLAB environment.
PROPER is an efficient tool for optimization and comparison of ranking classifiers, providing over 20 different two- and three-dimensional performance curves.
- Predictive modeling
- Scoring classifier
- Big data
- Structural genomics
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. Ranking or scoring predictors play central roles in a very wide range of biological data analysis problems, e.g. protein structure and function annotation, sequence alignment, genome annotation and many others. Most of biological datasets contain extensive noise that complicates predictive modeling. In different applications optimal predictors may differ depending on the purpose of specific study and characteristics of specific datasets. Thus, 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. PROPER also allows feature selection and evaluation of features’ importance using l1-regularized logistic regression  and Random Forest , respectively. At the same time PROPER allows semi-automated optimization of complex methods, such as Artificial Neural Network (ANN). Moreover, output of scoring classifiers currently not implemented in PROPER can be uploaded and used for performance visualization and comparison with available methods.
Several illustrative examples below demonstrate different features of PROPER. An example presented in Fig. 2 illustrates PROPERs functions, i.e. optimization, comparison, and visualization, applied to independent training and testing sets of data from a study on prediction of protein sequence crystallizability . In this study, we have used a dataset of 5691 protein sequences in negative set and 4924 protein sequences in positive set. For each protein sequence 48 different features were calculated and fed into machine learning methods. This data is available at http://ffas.burnham.org/XtalPred/help.html. After loading the data, optimization of model’s structure, e.g. selection of ANN learning algorithm, is performed by generating two- and three-dimensional performance curves and then similar curves are generated to compare performance of different optimized models. ANN training begins with initial random weights for each feature and, after each iteration, a learning algorithm changes these weights to reach the highest level of accuracy. Figure 2(a-d) shows differences in performance of four standard learning algorithms applied to training of ANN on this database. More examples and detailed information about installing PROPER is available from user manual that could be downloaded from the distribution directory at sourceforge.
In summary, PROPER is a freely available package for performance visualization, comparison and optimization of scoring classifiers in MATLAB. Performance visualization can be applied to output of any scoring classifier available or not available in PROPER. PROPER will be helpful in improving reproducibility and standardization of research in the field of biological Big Data outcome prediction.
Project name: ROPER
Operating system(s): Platform independent
Other requirements: It requires the MATLAB Statistics Toolbox
Any restrictions to use by non-academics: None
This work is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under Award No. R01 GM095847 (SER/XtalPred). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Liu J, Ji S, Ye J. SLEP: Sparse Learning with Efficient Projections. Arizona State University. 2009. http://www.public.asu.edu/~jye02/Software/SLEP/citation.htm.
- Breiman L. Random Forests. Mach Learn. 2001;45:5–32.View ArticleGoogle Scholar
- Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA Data Mining Software: An Update; SIGKDD Explorations. 2009; Volume 11, Issue 1. http://dl.acm.org/citation.cfm?id=1656278.
- Jahandideh S, Jaroszewski L, Godzik A. Improving the chances of successful protein structure determination with a random forest classifier. Acta Cryst D. 2014;70:627–35.View ArticleGoogle Scholar