{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:32:35Z","timestamp":1675297955060},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Accurate large-scale phenotyping has recently gained considerable importance in biology. For example, in genome-wide association studies technological advances have rendered genotyping cheap, leaving phenotype acquisition as the major bottleneck. Automatic image analysis is one major strategy to phenotype individuals in large numbers. Current approaches for visual phenotyping focus predominantly on summarizing statistics and geometric measures, such as height and width of an individual, or color histograms and patterns. However, more subtle, but biologically informative phenotypes, such as the local deformation of the shape of an individual with respect to the population mean cannot be automatically extracted and quantified by current techniques.<\/jats:p>\n               <jats:p>Results: We propose a probabilistic machine learning model that allows for the extraction of deformation phenotypes from biological images, making them available as quantitative traits for downstream analysis. Our approach jointly models a collection of images using a learned common template that is mapped onto each image through a deformable smooth transformation. In a case study, we analyze the shape deformations of 388 guppy fish (Poecilia reticulata). We find that the flexible shape phenotypes our model extracts are complementary to basic geometric measures. Moreover, these quantitative traits assort the observations into distinct groups and can be mapped to polymorphic genetic loci of the sample set.<\/jats:p>\n               <jats:p>Availability: Code is available under: http:\/\/bioweb.me\/GEBI<\/jats:p>\n               <jats:p>Contact: \u00a0theofanis.karaletsos@tuebingen.mpg.de; oliver.stegle@tuebingen.mpg.de<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts081","type":"journal-article","created":{"date-parts":[[2012,2,15]],"date-time":"2012-02-15T01:50:26Z","timestamp":1329270626000},"page":"1001-1008","source":"Crossref","is-referenced-by-count":4,"title":["ShapePheno: unsupervised extraction of shape phenotypes from biological image collections"],"prefix":"10.1093","volume":"28","author":[{"given":"Theofanis","family":"Karaletsos","sequence":"first","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Stegle","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Dreyer","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Winn","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karsten M.","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"},{"name":"1 Machine Learning and Computational Biology Research Group, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, 2Department of Molecular Biology, Max Planck Institute for Developmental Biology, 72076 T\u00fcbingen, Germany, 3Machine Learning and Perception Group, Microsoft Research Ltd, Cambridge CB3 0FB, UK and 4Zentrum f\u00fcr Bioinformatik, Eberhard Karls Universit\u00e4t, 72076 T\u00fcbingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,2,13]]},"reference":[{"key":"2023012512223020300_B1","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1111\/j.1420-9101.2004.00788.x","article-title":"Sexual isolation and extreme morphological divergence in the cuman guppy: a possible case of incipient speciation","volume":"17","author":"Alexander","year":"2004","journal-title":"J. Evolution. Biol."},{"key":"2023012512223020300_B2","volume-title":"Pattern Recognition and Machine Learning.","author":"Bishop","year":"2006"},{"key":"2023012512223020300_B3","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2023012512223020300_B4","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"Frey","year":"2007","journal-title":"Science"},{"key":"2023012512223020300_B5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1038\/msb.2010.25","article-title":"Clustering phenotype populations by genome-wide RNAi and multiparametric imaging","volume":"6","author":"Fuchs","year":"2010","journal-title":"Mol. Syst. Biol."},{"key":"2023012512223020300_B6","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1111\/j.1420-9101.2005.01061.x","article-title":"Parallel evolution of the sexes? Effects of predation and habitat features on the size and shape of wild guppies","volume":"19","author":"Hendry","year":"2006","journal-title":"J. Evolution. Biol."},{"key":"2023012512223020300_B7","first-page":"2006","article-title":"Clustering appearance and shape by learning jigsaws","volume-title":"Advances in Neural Information Processing Systems.","author":"Kannan","year":"2007"},{"key":"2023012512223020300_B8","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/TIP.2005.852470","article-title":"Toward automatic phenotyping of developing embryos from videos","volume":"14","author":"Ning","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"2023012512223020300_B9","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1093\/bioinformatics\/btq046","article-title":"Ebimage\u2014an R package for image processing with applications to cellular phenotypes","volume":"26","author":"Pau","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012512223020300_B10","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1093\/bioinformatics\/btn346","article-title":"Bioimage informatics: a new area of engineering biology","volume":"24","author":"Peng","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012512223020300_B11","doi-asserted-by":"crossref","first-page":"i57","DOI":"10.1093\/bioinformatics\/btq219","article-title":"As-rigid-as-possible mosaicking and serial section registration of large system datasets","volume":"26","author":"Saalfeld","year":"2010","journal-title":"Bioinformatics"},{"key":"2023012512223020300_B12","doi-asserted-by":"crossref","first-page":"e1000974","DOI":"10.1371\/journal.pcbi.1000974","article-title":"Pattern recognition software and techniques for biological image analysis","volume":"6","author":"Shamir","year":"2010","journal-title":"PLoS Comput. Biol."},{"key":"2023012512223020300_B13","doi-asserted-by":"crossref","first-page":"9440","DOI":"10.1073\/pnas.1530509100","article-title":"Statistical significance for genomewide studies","volume":"100","author":"Storey","year":"2003","journal-title":"Proc. Natl. Acad. Sci."},{"key":"2023012512223020300_B14","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1098\/rspb.2008.1930","article-title":"Genetic linkage map of the guppy, poecilia reticulata, and quantitative trait loci analysis of male size and colour variation","volume":"276","author":"Tripathi","year":"2009","journal-title":"P. Roy. Soc. B Bio."},{"key":"2023012512223020300_B15","volume-title":"PhD Thesis","author":"Tripathi","year":"2009"},{"issue":"Suppl. 3","key":"2023012512223020300_B16","doi-asserted-by":"crossref","first-page":"S26","DOI":"10.1038\/nmeth.1431","article-title":"Visualization of image data from cells to organisms","volume":"7","author":"Walter","year":"2010","journal-title":"Nat. Methods"},{"key":"2023012512223020300_B17","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1126\/science.1129161","article-title":"Evolutionary paths underlying flower color variation in antirrhinum","volume":"313","author":"Whibley","year":"2006","journal-title":"Science"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/7\/1001\/48882493\/bioinformatics_28_7_1001.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/28\/7\/1001\/48882493\/bioinformatics_28_7_1001.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T15:49:57Z","timestamp":1674661797000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/28\/7\/1001\/210966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,13]]},"references-count":17,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2012,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bts081","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2012,4,1]]},"published":{"date-parts":[[2012,2,13]]}}}