A new hybrid method for predicting protein interactions using Genetic Algorithms and Extended Kalman Filters

Protein-Protein Interactions (PPIs) play a very important role in many cellular processes and a variety of experimental approaches have been developed for their identification. These approaches however suffer from high error rates. Recently, computational methods have been employed to assist for the prediction. A common problem with the applied computational methods is that they either result in low predictive performances or produce “black box” classifiers that aren´t easily interpretable. In our method we combined Genetic Algorithms and Extended Kalman Filters in order to find the mathematical equation that governs the best classifier. As a result, we are able to construct hypotheses that can explain the complex relationships in the data and biological knowledge can be extrapolated in predictable ways. We tested our hybrid method with a commonly used data set and compared it to previous approaches. We achieved high classification measures (sensitivity: 75.04%, specificity: 84.95%) and outperformed the other methods.