Transmembrane (TM) proteins represent about 20-30% of the protein sequences in 
higher eukaryotes, playing important roles across a range of cellular functions. 
Moreover, knowledge about topology of these proteins often provides crucial 
hints toward their function. Due to the difficulties in experimental structure 
determinations of TM protein, theoretical prediction methods are highly 
preferred in identifying the topology of newly found ones according to their 
primary sequences, useful in both basic research and drug discovery. In this 
paper, based on the concept of pseudo amino acid composition (PseAA) that can 
incorporate sequence-order information of a protein sequence so as to remarkably 
enhance the power of discrete models (Chou, K. C., Proteins: Structure, 
Function, and Genetics, 2001, 43: 246-255), cellular automata and Lempel-Ziv 
complexity are introduced to predict the TM regions of integral membrane 
proteins including both alpha-helical and beta-barrel membrane proteins, 
validated by jackknife test. The result thus obtained is quite promising, which 
indicates that the current approach might be a quite potential high throughput 
tool in the post-genomic era. The source code and dataset are available for 
academic users at liml@scu.edu.cn.