title: Understandable learning machine system design for transmembrane or embedded membrane segments prediction.
abstract: We modified an existing association rule-based classifier CPAR to improve traditional black box model based learning machine approaches on Transmembrane (TM) segment prediction. The modified classifier was improved further by combining with SVM. The experimental results indicate that this hybrid scheme offers biologically meaningful rules on TM/EM segment prediction while maintaining the performance almost as well as the SVM method. The evaluation of the sturdiness and the Receiver Operating Characteristic (ROC) curve analysis proved that this new scheme is robust and competent with SVM on TM/EM segment prediction. The prediction server is available at http://bmcc2.cs.gsu.edu/ haeh2/.
authors.affiliation: Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA. haejin.hu@gmail.com
authors.forename: Hae-Jin
authors.initials: HJ
authors.lastname: Hu
authors.forename: Robert W
authors.initials: RW
authors.lastname: Harrison
authors.forename: Phang C
authors.initials: PC
authors.lastname: Tai
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D008565:Membrane Proteins / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D008565:Membrane Proteins