Detecting Transmembrane Proteins Using Decision Trees

Transmembrane (TM) proteins are proteins that span a cell membrane; their segments crossing the membrane are called TM domains. TM domain and TM protein detection are important problems in computational biology, but typical machine learning approaches yield classifiers that are difficult to interpret and hence yield no biological insight. We study both TM domain and TM protein detection with easy to interpret decision trees. For TM domain detection, the use of decision trees is already reported in the literature, but we provide a critical study of the existing approach, resulting in improved feature sets as well as observations on how to avoid biased training and test sets. In particular, we discover a motif known to be common to TM domains that was not discovered in previous research using machine learning. For TM protein detection, we propose a 2-layer learning method. This method can be generalized to deal with a large class of string classification problems. The method achieves sensitivity and specificity values of up to 92 % on the settings we experimented with, while providing intuitive classifiers that are easy to interpret for the domain expert.