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Aims to identify the complex relationships between phenotypes and environments. PhenoCurve splits the whole dataset into reliable and unreliable windows based on curve fitting, and then optimizes the results on the unreliable windows using regularized polynomial regression and maximum the posterior estimations. It is 1Qbased on Bayesian theorem and regularized polynomial regression.

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PhenoCurve classification

PhenoCurve specifications

Software type:
Restrictions to use:
Operating system:
Command line interface
Input data:
Raw phenotype and environment data
Computer skills:

PhenoCurve distribution


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Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA; Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, USA; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

Funding source(s)

This research was supported by the U.S. Department of Energy [no. DE-FG02-91ER20021], the National Science Foundation [no. 1458556], and MSU Center for Advanced Algal and Plant Phenotyping.

Link to literature

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