Feature weights of (a) and (b) attribute values with respect to indices and the distribution of attribute weights.

<p>Feature weights of (a) and (b) attribute values with respect to indices and the distribution of attribute weights.</p

A Consistency-Based Feature Selection Method Allied with Linear SVMs for HIV-1 Protease Cleavage Site Prediction

<div><p>Background</p><p>Predicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.</p><p>Results</p><p>We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.</p><p>Conclusion</p><p>Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.</p><p>Software availability</p><p>The software is available at <a href="http://ozyer.etu.edu.tr/c-fs-svm.rar" target="_blank">http://ozyer.etu.edu.tr/c-fs-svm.rar</a>.</p><p>The software can be downloaded at <a href="http://esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar" target="_blank">esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar</a>; you will find a readme file which explains how to set the software in order to work.</p></div

Detailed System Overview.

<p>Closer look at the various components of the proposed system architecture; orthonormal encoding is used to represent amino acids.</p

Selected attributes according to the FS methods (- indicates no feature is specifically determined for the column).

<p>Values common to all methods have been italicized.</p

Feature weights of (a) <i>P</i>₃' and (b) and <i>P</i>₄': attribute values with respect to indices and the distribution of attribute weights.

<p>Feature weights of (a) <i>P</i>₃' and (b) and <i>P</i>₄': attribute values with respect to indices and the distribution of attribute weights.</p

Standard Deviations of classification results for external cross validation with MLP and their average performance results for each metric.

<p>Standard Deviations of classification results for external cross validation with MLP and their average performance results for each metric.</p

Standard Deviations of classification results for external cross validation with SMO and their average performance results for each metric.

<p>Standard Deviations of classification results for external cross validation with SMO and their average performance results for each metric.</p

Feature weights of (a) <i>P</i>₁' and (b) <i>P</i>₂': attribute values with respect to indices and the distribution of attribute weights.

<p>Feature weights of (a) <i>P</i>₁' and (b) <i>P</i>₂': attribute values with respect to indices and the distribution of attribute weights.</p

Overall System Architecture.

<p>The input data is preprocessed then the preprocessed data may be directly classified or feature selection is applied to utilize in the classification only relevant features.</p

Feature weights of (a) <i>P</i>₄ and (b) and <i>P</i>₃.

<p>Feature weights of (a) <i>P</i>₄ and (b) and <i>P</i>₃.</p