ReviewFrontiers in genetics2019
A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 130 citations in OpenAlex.
- Potential Applications of Genome-Wide Association Studies in Establishing Climate Resilience in Livestock: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Artificial intelligence in animal breeding and genetics: applications, opportunities, and challenges.Animal frontiers : the review magazine of animal agriculture · 2026Article
- The Effect of Artificial Insemination and Multiple Ovulation Embryo Transfer on Production, Health Status, and Survival of Holstein-Friesian Cows.Veterinary sciences · 2026Article
- FSBLUP: a novel strategy of fusion similarity matrix construction via optimally integrating intermediate omics data to enhance genomic prediction.Genome biology · 2026Article
- Challenges and opportunities: computational biology and the future of agriculture.Bioinformatics advances · 2026Article
- PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies.Genomics, proteomics & bioinformatics · 2025Article
- Multi-trait phenotypic modeling through factor analysis and bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers.Frontiers in genetics · 2025Article
- Capturing resilience from phenotypic deviations: a case study using feed consumption and whole genome data in pigs.BMC genomics · 2024Article
- How do pig veterinarians view technology-assisted data utilisation for pig health and welfare management? A qualitative study in Spain, the Netherlands, and Ireland.Porcine health management · 2024Article
- Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.Animals : an open access journal from MDPI · 2024Article
- A first characterization of the microbiota-resilience link in swine.Microbiome · 2024Article
- Current challenges and future of agricultural genomes to phenomes in the USA.Genome biology · 2024Article
- Animal Wellness: The Power of Multiomics and Integrative Strategies: Multiomics in Improving Animal Health.Veterinary medicine international · 2024Review
- Digital Phenotyping: A Game Changer for the Broiler Industry.Animals : an open access journal from MDPI · 2023Review
- Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation.Sensors (Basel, Switzerland) · 2023Review
- A multi-omics analysis identifies molecular features associated with fertility in heifers (Bos taurus).Scientific reports · 2023Article
- The effects of breeding and selection on lactation in dairy cattle.Animal frontiers : the review magazine of animal agriculture · 2023Article
- Passive radio frequency identification and video tracking for the determination of location and movement of broilers.Poultry science · 2023Article
- Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming.Journal of animal science · 2023Article
- Determination of Non-Digestible Parts in Dairy Cattle Feces Using U-NET and F-CRN Architectures.Veterinary sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in Agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, "big data," analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g., "big data" training), data (e.g., data transfer, management, and analytics), and technology (e.g., development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics, and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food in a rapidly growing population.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.