An Agent-Based Model to Associate Genomic and Environmental Data for Phenotypic Prediction in Plants

Title:An Agent-Based Model to Associate Genomic and Environmental Data for Phenotypic Prediction in Plants

Volume: 11 Issue: 5

Author(s): Sebastien Alameda, Jean-Pierre Mano, Carole Bernon and Sebastien Mella

Affiliation:

Keywords: Adaptation, environmental data, genomics, multi-agent systems, phenotypic prediction.

Abstract: Background: One of the means to increase in-field crop yields is the use of software tools to predict future yield values using past in-field trials and plant genetics. The traditional, statistics-based approaches lack environmental data integration and are very sensitive to missing and/or noisy data.

Objective: In this paper, we show that a cooperative, adaptive Multi-Agent System can overcome the drawbacks of such algorithms.

Method: The system resolves the problem in an iterative way by a cooperation between the constraints, modelled as agents.

Results: Results show that the Agent-Based Model gives results comparable to other approaches, without having to preprocess or reconcile data.

Conclusion: This collective and self-adaptive search of a solution functions like a heuristic to efficiently explore the solution space and is therefore able to consider both genetic and environmental data.

Cite this article as:

Alameda Sebastien, Mano Jean-Pierre, Bernon Carole and Mella Sebastien, An Agent-Based Model to Associate Genomic and Environmental Data for Phenotypic Prediction in Plants, Current Bioinformatics 2016; 11 (5) . https://dx.doi.org/10.2174/1574893611666160617094329