Membrane　proteins　are　central　to　carrying　out　impressive　biological　functions.　In　general,　accurate　knowledge　of　transmembrane　(TM)　regions　facilitates　ab　initio　folding　and　functional　annotations　of　membrane　proteins.　Therefore,　large-scale　locating　of　TM　regions　in　membrane　proteins　by　wet　experiments　is　needed;　however,　it　is　hampered　by　practical　difficulties.　In　this　context,　in　silico　methods　for　TM　prediction　are　highly　desired.　Here,　we　present　a　TM　region　prediction　method　using　machine　learning　algorithms　and　sequence　evolutionary　profiles.　Hydrophobic　properties　were　also　assessed.　Furthermore,　a　combined　method　using　sequence　evolutionary　profiles　and　hydrophobicity　measures　was　tested.　The　model　was　intensively　trained　on　large　datasets　by　means　of　neural　network　and　random　forest　learning　algorithms　for　TM　region　prediction.　The　proposed　method　can　be　directly　applied　to　identify　membrane　proteins　from　proteome-wide　sequences.　Benchmark　results　suggest　that　our　method　is　an　attractive　alternative　to　membrane　protein　prediction　for　real-world　applications.　The　web　server　and　stand-alone　program　of　the　proposed　method　are　publicly　available　at　http://genomics.fzu.edu.cn/nnme/index.html.