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Feature relationships are complex and may contain important information. k top scoring pairs ( k -TSP) studies feature relationships by the horizontal comparison. This study examines feature relationships and proposes vertical and horizontal k -TSP (VH- k -TSP) to identify the discriminative feature pairs by evaluating feature pairs based on the vertical and horizontal comparisons. Complexity is introduced to compute the discriminative abilities of feature pairs by means of these two comparisons. VH- k -TSP was compared with support vector machine-recursive feature elimination, relative simplicity-support vector machine, k -TSP and M- k -TSP on nine public genomics datasets. For multi-class problems, one-to-one method was used. The experiments showed that VH- k -TSP outperformed the four methods in most cases. Then, VH- k -TSP was applied to a metabolomics data of liver disease. An accuracy rate of 88.11 ± 3.30% in discrimination between cirrhosis and hepatocellular carcinoma was obtained by VH- k -TSP, better than 77.39 ± 4.10% and 79.28 ± 3.73% obtained by k -TSP and M- k -TSP, respectively. Hence combining the vertical and horizontal comparisons could define more discriminative feature pairs. [ABSTRACT FROM AUTHOR]

Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data. [ABSTRACT FROM AUTHOR]