HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system

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@Article{Majid:2015:AA,

• author = "Abdul Majid and Safdar Ali",
• title = "HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system",
• year = "2015",
• journal = "Amino Acids",
• volume = "47",
• number = "1",
• pages = "217--221",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Breast cancer diagnosis, Protein sequences, Amino acids, Physicochemical properties, Evolutionary ensemble system, PSO",
• language = "English",
• publisher = "Springer",
• ISSN = "0939-4451",
• URL = "http://dx.doi.org/10.1007/s00726-014-1871-3",
• DOI = "doi:10.1007/s00726-014-1871-3",
• size = "5 pages",
• 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.",
• notes = "PMID: 25488423 [PubMed - indexed for MEDLINE]",

}

Genetic Programming entries for Abdul Majid Safdar Ali

Citations