Avaliação de métodos de imputação de valores ausentes para a predição de interações fármaco-proteína

Abstract

In the last decade, the study of pharmacological networks has received a lot of attention given its relevance drug discovery process. Many different approaches for predicting biological interactions have been proposed, especially in the area of multiple kernel learning (MKL). Such methods comprise integrative approaches that can handle heterogeneous data sources, but suffer from the missing data problem. Techniques to handle missing values in the base kernel matrices can be used, usually based on simple techniques, such as imputing zeroes, mean and median of the matrix. In this work, techniques for handling missing values were evaluated in the context of bipartite networks. Our analyzes showed that the, depending on the amount of missing data, k-NN and SVD technique performed much better than the other techniques, bringing encouraging results, while zero-fill showed the worst performance in relation to all other evaluated methods.