- 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 = "
http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p347.pdf",
-
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 \cite{1389156}
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 http://hdl.handle.net/10016/15508 Universidad Carlos III de Madrid. Departamento de Informatica 'Anotacion funcional de proteinas basada en representacion relacional en el entorno de la biologia de sistemas'",
