Protein-protein functional association prediction using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Beatriz Garcia and Ricardo Aler and 
                 Agapito Ledezma and Araceli Sanchis",
  title =        "Protein-protein functional association prediction
                 using genetic programming",
  booktitle =    "GECCO '08: Proceedings of the 10th annual conference
                 on Genetic and evolutionary computation",
  year =         "2008",
  editor =       "Maarten Keijzer and Giuliano Antoniol and 
                 Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and 
                 Nikolaus Hansen and John H. Holmes and 
                 Gregory S. Hornby and Daniel Howard and James Kennedy and 
                 Sanjeev Kumar and Fernando G. Lobo and 
                 Julian Francis Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Jordan Pollack and Kumara Sastry and 
                 Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and 
                 Ingo Wegener",
  isbn13 =       "978-1-60558-130-9",
  pages =        "347--348",
  address =      "Atlanta, GA, USA",
  URL =          "",
  DOI =          "doi:10.1145/1389095.1389156",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  abstract =     "Determining if a group of proteins are functionally
                 associated among themselves is an open problem in
                 molecular biology. Within our long term goal of
                 applying Genetic Programming (GP) to this domain, this
                 paper evaluates the feasibility of GP to predict if a
                 given pair of proteins interacts. GP has been chosen
                 because of its potential flexibility in many aspects,
                 such as the definition of operations. In this paper,
                 the if-unknown operation is defined, which semantically
                 is the most appropriate in this domain for handling
                 missing values. We have also used the Tarpeian bloat
                 control method to decrease the computational time and
                 the solution size. Our results show that GP is feasible
                 for this domain and that the Tarpeian method can obtain
                 large improvements in search efficiency and
                 interpretability of solutions.",
  keywords =     "genetic algorithms, genetic programming,
                 bioinformatics, classifier systems, control bloat, data
                 integration, evolutionary computation, machine
                 learning, protein interaction prediction, computational
                 biology: Poster",
  notes =        "GECCO-2008 A joint meeting of the seventeenth
                 international conference on genetic algorithms
                 (ICGA-2008) and the thirteenth annual genetic
                 programming conference (GP-2008).

                 ACM Order Number 910081. Also known as

                 E.coli. Training on subsets 5000 positive and 5000
                 negatives. Missing values if_? if(unknown) then else.
                 p348 GP about as accruate as other machine learning.
                 ADTree KODE KStar MLP PART Simple Logistic SMO.
                 Tarpeian bloat control \cite{poli03} effective.

                 PhD Universidad
                 Carlos III de Madrid. Departamento de Informatica
                 'Anotacion funcional de proteinas basada en
                 representacion relacional en el entorno de la biologia
                 de sistemas'",

Genetic Programming entries for Beatriz Garcia Ricardo Aler Mur Agapito Ledezma Araceli Sanchis