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.