Inter-functional analysis of high-throughput phenotype data by non-parametric clustering and its application to photosynthesis | Zendy

Qiaozi Gao | Zendy; Elisabeth Ostendorf | Zendy; Jeffrey A. Cruz | Zendy; Rong Jin | Zendy; David Kramer | Zendy; Jin Chen | Zendy

Open Access

Inter-functional analysis of high-throughput phenotype data by non-parametric clustering and its application to photosynthesis

Author(s) -

Qiaozi Gao,

Elisabeth Ostendorf,

Jeffrey A. Cruz,

Rong Jin,

David Kramer,

Jin Chen

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv515

Subject(s) - phenomics , cluster analysis , computational biology , computer science , phenotype , hierarchical clustering , throughput , parametric statistics , biology , data mining , artificial intelligence , genetics , genomics , statistics , genome , gene , mathematics , telecommunications , wireless

Phenomics is the study of the properties and behaviors of organisms (i.e. their phenotypes) on a high-throughput scale. New computational tools are needed to analyze complex phenomics data, which consists of multiple traits/behaviors that interact with each other and are dependent on external factors, such as genotype and environmental conditions, in a way that has not been well studied.

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