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Enregistrement W3014109958 · doi:10.1093/af/vfaa013

The Future of Phenomics

2020· editorial· en· W3014109958 sur OpenAlexaff
Christine F. Baes, Flávio S. Schenkel

Notice bibliographique

RevueAnimal Frontiers · 2020
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNutrition, Genetics, and Disease
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésPhenomicsComputational biologyBiologyGeneticsGenomicsGene

Résumé

récupéré en direct d'OpenAlex

Advances in genomics have led to an improved understanding of genetic variation in livestock production traits. In this context, collection of high-throughput, accurate phenotypic data has become the limiting factor in livestock genomics and related fields. To improve understanding of the complex interactions and underlying biological and physiological systems within livestock species, improved trait definitions of specific, economically relevant phenotypes are required. Collecting both high-density phenotypic and environmental data is therefore a major challenge for livestock production research. Novel phenotypes of interest, from gene expression to animal product characteristics, need to be identified, standardized, and their collection automated in computable formats. Development of high-throughput data collection techniques from multiple research disciplines at different biological levels is required. Research networks between academia, government, and the private sector should enhance scientific collaboration and catalyze development of modern data sharing policies. New bioinformatics approaches and advanced data management, processing, and analysis methods have become essential for integrating and interpreting the large amounts of data generated by multiple sources. Such unprecedented advances should allow a better understanding of the phenome, as well as advances to economically important traits for livestock production systems. Included in this issue of Animal Frontiers are seven review articles showcasing how phenomics will impact livestock production in the future (Figure 1). The contributions from Africa, Europe, Asia, and the Americas provide a global perspective of how livestock scientists view the automation of phenotype recording. The future of phenomics will include development of high-throughput data collection techniques from multiple research disciplines at different biological levels, collection of environmental data, and new computational approaches to integrate and interpret large amounts of data. The first two reviews offer contributions from Kenya and South Africa. Dr Raphael Mrode from the International Livestock Research Institute (Kenya) and Scotland’s Rural College (United Kingdom) and his colleagues provide excellent insight into how digital technology could change livestock development in low-income countries by examining innovative applications of emerging trends (Mrode et al., 2020). Dr Carina Visser and her colleagues from the University of Pretoria describe phenomics for sustainable production in the South African beef and dairy cattle industry (Visser et al., 2020). We then move to Europe, where Mike Coffey from Scotland’s Rural College (United Kingdom) coined the phrase “in the age of the genotype, #PhenotypeIsKing”, a hashtag that has been widely spread throughout the genetics and genomics world (Coffey, 2020). Dr Anita Seidel and her colleagues from the Christian Albrecht University in Kiel, Germany provide insight into dealing with complexity in modern dairy cattle breeding (Seidel et al., 2020). Dr Yachun Wang and her colleagues from China Agricultural University describe future opportunities and their implications for genetic improvement of temperament traits in dairy cattle (Chang et al., 2020). From there, Dr John Cole of the United States Department of Agriculture and collaborators describe the future of phenomics in the American dairy cattle industry (Cole et al., 2020). The issue is completed with Dr Ricardo Ventura and his team’s description of the opportunities and challenges of phenomics applied to livestock and aquaculture breeding in South America (Ventura et al., 2020). The overall goal of this issue of Animal Frontiers is to provide insight into emerging trends in livestock phenomics and to offer viewpoints from some of the leading researchers in the field on how to use phenomics in livestock agriculture. It is clear that the pressure to improve animal housing and breeding strategies will only increase in the future, so the need to critically evaluate new strategies at the farm level is imperative. The initial research findings showcased in this issue are exciting and suggest that the future of data collection using new approaches and technologies is a bright one. Precision phenomics will not come from one technology, but an integrated approach involving many different levels of farm management, public policy, and industry commitment. Are you ready for the future? Christine Baes is an associate professor at the University of Guelph, NSERC Canada Research Chair in Livestock Genomics, and 2020 President of the Canadian Society of Animal Science (CSAS). She completed her PhD at the Christian Albrechts University in Kiel, Germany and worked for a number of breeding and genetics companies in Germany and Switzerland prior to returning to academia at the University of Guelph. Her current research focuses on the development of breeding programs to improve the health, welfare, and productivity of dairy cattle and poultry. Corresponding author:cbaes@uoguelph.ca Flavio Schenkel is Professor at the University of Guelph, where he serves as the Director of the Centre for Genetic Improvement of Livestock, and is 2020 President Elect of the Canadian Society of Animal Science. He serves on a number of influential academic and industry boards in Canada. His research interests range from theoretical to applied genetics and genomics in livestock breeding, with a current focus on the use of genomic information to enhance genetic evaluation of livestock species with emphasis on genomic selection.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,023
score de la tête « metaresearch » (Gemma)0,048
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,120

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0230,048
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0030,001
Études des sciences et des technologies0,0020,008
Communication savante0,0100,009
Science ouverte0,0030,004
Intégrité de la recherche0,0150,032
Charge utile insuffisante (le modèle a refusé de juger)0,0110,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,003
Tête enseignante GPT0,212
Écart entre enseignants0,209 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2020
Routes d'admission1
Résumé présentnon

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