Inference of large-scale structural features of gene regulation networks using genetic algorithms

Considerable attempts have been made to develop models and learning strategies to infer gene networks starting from single connections. However, due to noise and other difficulties that arise from making measurements at the meso and nano levels, these so called bottom-up approaches have not been of much success. The need for methods that use a top-down approach to extract global statistics from expression data has emerged to deal with such difficulties. This paper presents a theoretical framework that employs global statistics learnt from gene expression data to infer different network structural properties of large- scale gene regulatory networks. The framework is inspired by genetic algorithms and designed with the aim to address the different weaknesses in existing approaches. Experimental results show that the developed system is more superior to previously published results.

[1]  Mark Schena,et al.  DNA microarrays : a practical approach , 1999 .

[2]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[3]  M. Kramer,et al.  Clinical Epidemiology and Biostatistics , 1988, Springer Berlin Heidelberg.

[4]  Andreas Wagner,et al.  Estimating coarse gene network structure from large-scale gene perturbation data. , 2002, Genome research.

[5]  Stephen F. Smith,et al.  Competition-Based Induction of Decision Models from Examples , 2004, Machine Learning.

[6]  John N. Weinstein,et al.  Inference of large-scale topology of gene regulation networks by neural nets , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[7]  Albert Y. Zomaya.  Handbook of Nature-Inspired and Innovative Computing - Integrating Classical Models with Emerging Technologies , 2006 .

[8]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[9]  Evlin L. Kinney,et al.  Clinical Epidemiology and Biostatistics: A Primer for Clinical Investigators and Decision-Makers , 1989 .

[10]  Hitoshi Iba,et al.  Inference of gene regulatory model by genetic algorithms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Arantxa Etxeverria.  The Origins of Order , 1993 .

[13]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[14]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[15]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[16]  H. Iba,et al.  Inferring a system of differential equations for a gene regulatory network by using genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[17]  M Wahde,et al.  Coarse-grained reverse engineering of genetic regulatory networks. , 2000, Bio Systems.

[18]  Ian H. Witten,et al.  Weka: Practical machine learning tools and techniques with Java implementations , 1999 .

[19]  R. Wallace.  Is this a practical approach? , 2001, Journal of the American College of Surgeons.

[20]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[21]  Zoltan Szallasi,et al.  Genetic Network Analysis in Light of Massively Parallel Biological Data Acquisition , 1998, Pacific Symposium on Biocomputing.