Implementation and comparison of kernel-based learning methods to predict metabolic networks

Abstract

Metabolic pathways can be conceptualized as the biological equivalent of a data pipeline. In living cells, series of chemical reactions are carried out by different proteins called enzymes in a stepwise manner. However, many pathways remain incompletely characterized, and in some of them, not all enzyme components have been identified. Kernel methods are useful in many difficult problem areas, such as document classification and bioinformatics. Specifically, kernel methods have been used recently to predict biological networks, such as protein-protein interaction networks and metabolic networks. In this paper, we implement and compare different methods and types of data to predict metabolic networks. The methods are Penalized Kernel Matrix Regression (PKMR) and pairwise Support Vector Machine (pSVM). We develop several experiments using these methods with sequence, non-sequence, and combined data. We obtain better accuracy when the sequence data are used in both methods. Whereas when the methods are compared using the same type of data, the pSVM approach shows better accuracy. The best results are obtained with pSVM using all combined kernels.

Keywords: Kernel methods; Machine learning; Metabolic pathways; Network prediction.

Fig. 1

Comparison of the methods (PKMR—Penalized Kernel Matrix Regression and pSVM—pairwise Support Vector Machine) for the sequence data kernels, related to accuracy and execution times