Classify Rab proteins (GTPases) using ML approach
Our project aimed to build a classifier for the Rab proteins. We tried 3 machine learning methods: k nearest neighbour, decision tree, random forest. To use them, we translated our amino acid sequences (source Tracy database of Fassauer Lab) by extracting features. We trained our model then tested its performance. We optimised our model with cross validation, over/undersampling to get an even distribution and by adding a non rab group. The best performing model was KNN (with k neighbours = 11) using the CKSAAP feature (with default k space = 3).
