Predicting the solvent accessible surface area (ASA) of transmembrane (TM) 
residues is of great importance for experimental researchers to elucidate 
diverse physiological processes. TM residues fall into two major structural 
classes (α-helix membrane protein and β-barrel membrane protein). The reported 
solvent ASA prediction models were developed for these two types of TM residues 
respectively. However, this prevents the general use of these methods because 
one cannot determine which model is suitable for a given TM residue without 
information of its type. To conquer this limitation, we developed a new 
computational model that can be used for predicting the ASA of both TM α-helix 
and β-barrel residues. The model was developed from 78 α-helix membrane protein 
chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by 
cross validation method and its prediction result on an independent test set of 
20 membrane protein chains. The results show that our model performs well for 
both types of TM residues and outperforms other prediction model which was 
developed for the specific type of TM residues. The prediction results also 
proved that the random forest model incorporating conservation score is an 
effective sequence-based computational approach for predicting the solvent ASA 
of TM residues.