Protein-protein functional association prediction using genetic programming

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.

REFERENCES

• 1: Koza J. Genetic Programming II. MIT Press, 1994.
• 2: Poli R. A Simple but Theoretically--Motivated Method to Control Bloat in Genetic Programming. In Proceedings of EuroGP'03. Springer Berlin, (Apr 2003), 43--76.
• 3: Valencia A. and Pazos F. Computational methods for the prediction of protein interactions. Curr. Opin. Struct. Biol., 12, 3 (Jun 2002), 368--373.
• 4: Ian Witten, Data Mining, Elsevier Science Inc., New York, NY, 2005
• 5: Zongker D. and Punch B. lil-gp. Michigan State University, Michigan, 1998. http://garage.cse.msu.edu/software/lil-gp/.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.2 Automatic Programming
          Subjects: Program synthesis

Additional Classification:
  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Biology and genetics

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
bioinformatics, classifier system, control bloat, data integration, evolutionary computation, genetic programming, machine learning, protein interaction prediction