2015
DOI: 10.1371/journal.pbio.1002033
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Abstract: Imagine if we could compute across phenotype data as easily as genomic data; this article calls for efforts to realize this vision and discusses the potential benefits.

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Cited by 194 publications
(183 citation statements)
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References 85 publications
(82 reference statements)
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“…The code and coded is getting manifested into a complex reality of interwoven coding (environment)-encoding (phenom)-decoding (genome) universe of environmentally mediated interactions. The information contents of phenome dwarves those of genome (Deans et al 2015).…”
Section: Phenotype Versus Genotypementioning
confidence: 99%
See 1 more Smart Citation
“…The code and coded is getting manifested into a complex reality of interwoven coding (environment)-encoding (phenom)-decoding (genome) universe of environmentally mediated interactions. The information contents of phenome dwarves those of genome (Deans et al 2015).…”
Section: Phenotype Versus Genotypementioning
confidence: 99%
“…Hightech and high-throughput phenotyping is getting more and more available and employed in several fields of phenomics (Sozzani & Benfey 2011). The vast and diverse landscape of phenotype data need to be processed by cyber-infrastructure broadly accessible and fed by computable phenoltype descriptions based on ontology terms and entity-quality formalism; by semantically represented phenotype data; by sets of processing algorithms combining logics of ontologies with statistics (Deans et al 2015). It is the time to change how we describe biodiversity to ensure phenotype computable and linkable to digital data with semantic, extensible, and broadly accessible 12 contents (Deans et al 2012a).…”
Section: Phenotype Versus Genotypementioning
confidence: 99%
“…This is due to the fact that, regardless of how they are collected, phenotype data are inherently complex, given that they can range from populations to individual plants to specific tissues and can be recorded on comparative, discrete, or continuous scales. Because the data are essentially infinite in both diversity and scale, data documentation, integration, representation, and accessibility are critical aspects that present significant challenges (for review, see Deans et al, 2015). In order to be broadly useful, large-scale, high-dimensional data sets must be represented in such a way that data can be easily aggregated and extracted to address biologically questions.…”
Section: Kdsmart])mentioning
confidence: 99%
“…While semantic annotation and open access publishing are likely to become an integral part of modern scientific workflows, standardization across data sets and domains remains in its infancy [12]. We expect that the semantic annotation of TraitBank resources will long remain a work in progress.…”
Section: Implications For Interoperabilitymentioning
confidence: 99%