{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:36:43Z","timestamp":1774492603796,"version":"3.50.1"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T00:00:00Z","timestamp":1485302400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1458556"],"award-info":[{"award-number":["1458556"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DE-FG02-91ER20021"],"award-info":[{"award-number":["DE-FG02-91ER20021"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The results on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>Software is available at phenomics.uky.edu\/PhenoCurve<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw673","type":"journal-article","created":{"date-parts":[[2016,10,21]],"date-time":"2016-10-21T19:05:23Z","timestamp":1477076723000},"page":"1370-1378","source":"Crossref","is-referenced-by-count":8,"title":["PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data"],"prefix":"10.1093","volume":"33","author":[{"given":"Yifan","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA"},{"name":"Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, USA"}]},{"given":"Zheyun","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA"}]},{"given":"Jeffrey A","family":"Cruz","sequence":"additional","affiliation":[{"name":"Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, USA"}]},{"given":"Linda J","family":"Savage","sequence":"additional","affiliation":[{"name":"Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, USA"}]},{"given":"David M","family":"Kramer","sequence":"additional","affiliation":[{"name":"Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, USA"},{"name":"Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA"}]},{"given":"Jin","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Informatics, University of Kentucky, Lexington, KY, USA"}]}],"member":"286","published-online":{"date-parts":[[2017,1,18]]},"reference":[{"key":"2023020205023069800_btw673-B1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1104\/pp.109.148494","article-title":"Large-scale reverse genetics in arabidopsis: case studies from the chloroplast 2010 project","volume":"152","author":"Ajjawi","year":"2010","journal-title":"Plant Physiol"},{"key":"2023020205023069800_btw673-B2","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1126\/science.1086391","article-title":"Genome-wide insertional mutagenesis of arabidopsis thaliana","volume":"301","author":"Alonso","year":"2003","journal-title":"Science"},{"key":"2023020205023069800_btw673-B3","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1111\/j.1365-3040.2007.01680.x","article-title":"Determining the limitations and regulation of photosynthetic energy transduction in leaves","volume":"30","author":"Baker","year":"2007","journal-title":"Plant Cell Env"},{"key":"2023020205023069800_btw673-B4","volume-title":"Nonlinear Regression: Iterative Estimation and Linear Approximations","author":"Bates","year":"1988"},{"key":"2023020205023069800_btw673-B5","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"key":"2023020205023069800_btw673-B6","volume-title":"Introduction to Bayesian Statistics","author":"Bolstad","year":"2013"},{"key":"2023020205023069800_btw673-B7","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0304-4076(96)01818-0","article-title":"An r-squared measure of goodness of fit for some common nonlinear regression models","volume":"77","author":"Cameron","year":"1997","journal-title":"J. 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