Tratamento de Kernels incompletos em redes bipartidas na predição de interações em redes biológicas

Abstract

In the last decade, the study of pharmacological networks has received a lot of attention given its relevance to the production of new drugs. The studies were made possible by the large volume of biological data generated, making it possible to understand and extract knowledge from them. However, although interesting, this is a process that brings with it some barriers in terms of viability, particularly when the data appear heterogeneously and contain missing information. Many different approaches for predicting biological interactions have been proposed, especially in the area of multiple kernel learning (Multiple Kernel Learning (MKL). The use of MKL methods in biological data is also compromised by the heterogeneity of data sources, but associated with the methods, techniques for complementing missing values in the base kernel matrices can be used, this filling process is usually done with simple techniques, such as imputing zeroes, mean and median of the matrix. In this work, techniques for handling false values were evaluated in the context of bipartite networks to solve the limitations related to the heterogeneity of the data. We used three single value imputation techniques (mean, median and zero) and a more complex predictive imputation technique (SVD). All the aforementioned techniques have already been used for matrix completeness in the context of unipartite networks. Our analyzes showed that the SVD technique performed much better than the other techniques in evaluative metrics, bringing encouraging results for the use of the technique in models that use bipartite networks. The average and median techniques showed similar performances, but lower than the SVD. And filling with zero showed the worst performance in relation to all other applied techniques.