title: HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system.
abstract: We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.
authors.affiliation: Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, 45650, Nilore, Pakistan, abdulmajiid@pieas.edu.pk.
authors.forename: Abdul
authors.initials: A
authors.lastname: Majid
authors.forename: Safdar
authors.initials: S
authors.lastname: Ali
mesh_terms: D000595:Amino Acid Sequence; D005075:Biological Evolution; D001943:Breast Neoplasms / Q000235:genetics*; D018567:Breast Neoplasms, Male / Q000235:genetics*; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D030562:Databases, Protein; D005260:Female; D006801:Humans; D008297:Male; D011506:Proteins / Q000737:chemistry / Q000235:genetics*
chemical_list: D011506:Proteins