title: 'TransMem': a neural network implemented in Excel spreadsheets for predicting transmembrane domains of proteins.
abstract: BACKGROUND
  Genomic sequences from different organisms, even prokaryotic, have plenty of orphan ORFs, making necessary methods for the prediction of protein structure and function. The prediction of the presence of hydrophobic transmembrane (HTM) stretches is a valuable clue for this.
  
  
  RESULTS
  The program. TransMem, based on a neural network and running on personal computers (either Apple Macintosh or PC, using Excel worksheets), for the prediction and distribution of amino acid residues in transmembrane segments of integral membrane proteins is reported. The percentage of residue predictive accuracy obtained for the set of proteins tested is 93%, ranging from 99.9% for the best to 71.7% for the worst prediction. The segment-based accuracy is 93.6%; 63.6% of the protein set match any of the predicted and observed segment locations.
  
  
  BACKGROUND
  TransMem is available upon request or by anonymous up: IP address: luz.uab.es, directory/pub/ TransMem. It is also placed on the EMBL file server (ftp:/(/)ftp.ebi.ac.uk/pub/software/mac/TransMem ).
authors.affiliation: Departament de Bioquímica i Biologia Molecular, Universitat Autonoma de Barcelona, Spain. patrick@luz.uab.es
authors.forename: P
authors.initials: P
authors.lastname: Aloy
authors.forename: J
authors.initials: J
authors.lastname: Cedano
authors.forename: B
authors.initials: B
authors.lastname: Oliva
authors.forename: F X
authors.initials: FX
authors.lastname: Avilés
authors.forename: E
authors.initials: E
authors.lastname: Querol
mesh_terms: D000818:Animals; D003628:Database Management Systems; D016208:Databases, Factual; D006801:Humans; D008565:Membrane Proteins / Q000737:chemistry*; D015394:Molecular Structure; D016571:Neural Networks, Computer; D012984:Software
chemical_list: D008565:Membrane Proteins