Kernel Based Approaches to Identify Hidden Connections in Gene Networks Using NetAnalyzer

Abstract

The latest advances in biotechnology are increasing the size and number of biological databases, specially those related to “omics” sciences. This data can be used to generate complex interaction networks, which analysis allows to extract biological information. Network analysis comprises a current bioinformatics challenge and the implementation of kernels offers a potential procedure to perform this analysis. Kernel algebraic functions have been used to study interaction networks and they are of major interest in new applications to improve machine learning studies. To manage these interaction networks, the NetAnalyzer tool was developed with the purpose of analysing multi-layer networks, calculating different probabilistic indices to establish the association between pairs of nodes. In this study we implement different kernel operations using several programming languages to inspect their reliability to perform these operations in different scenarios. Best performances have been included as a kernel functional module into NetAnalyzer, and we used them over gene interactions networks and gene-disease knowledge to identify disease causing genes.