Evidence map›Paper›PMID 37627019›Full record

ArticleBiology2023

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates.

Tim Young, Olivier Laroche, Seumas P Walker, Matthew R Miller, Paula Casanovas, Konstanze Steiner, Noah Esmaeili, Ruixiang Zhao, John P Bowman, Richard Wilson and 5 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
5.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 2 countries.

Tim YoungAquaculture Biotechnology Research Group, Department of Environmental Science, School of Science, Private Bag 92006, Auckland 1142, New Zealand.
Olivier LarocheCawthron Institute, Nelson 7010, New Zealand.ORCID 0000-0003-0755-0083
Seumas P WalkerCawthron Institute, Nelson 7010, New Zealand.
Matthew R MillerCawthron Institute, Nelson 7010, New Zealand.ORCID 0000-0002-5402-7466
Paula CasanovasCawthron Institute, Nelson 7010, New Zealand.
Konstanze SteinerCawthron Institute, Nelson 7010, New Zealand.
Noah EsmaeiliInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart Private Bag 49, Hobart 7005, Australia.ORCID 0000-0001-8704-939X
Ruixiang ZhaoInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart Private Bag 49, Hobart 7005, Australia.
John P BowmanTasmanian Institute of Agricultural Research, University of Tasmania, Hobart 7005, Australia.
Richard WilsonCentral Science Laboratory, Research Division, University of Tasmania, Hobart 7001, Australia.ORCID 0000-0003-0152-4394
Andrew BridleInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart Private Bag 49, Hobart 7005, Australia.
Chris G CarterInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart Private Bag 49, Hobart 7005, Australia.ORCID 0000-0001-5210-1282
Barbara F NowakInstitute for Marine and Antarctic Studies, University of Tasmania, Hobart Private Bag 49, Hobart 7005, Australia.ORCID 0000-0002-0347-643X
Andrea C AlfaroAquaculture Biotechnology Research Group, Department of Environmental Science, School of Science, Private Bag 92006, Auckland 1142, New Zealand.
Jane E SymondsCawthron Institute, Nelson 7010, New Zealand.ORCID 0000-0001-8307-423X
University of Tasmania · AUCawthron Institute · NZAuckland University of Technology · NZNew Zealand Institute for Public Health and Forensic Science · NZ

Funding

Ministry of Business, Innovation and Employment CAWX1606
6 · The paper itself

Abstract

Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (

Indexed as

aquaculturefeed efficiencyintegrated multi-omicsmachine learningmetabolomicsmicrobiomicsproteomicsrandom forest

Identifiers

PMID37627019
PMCPMC10452023
OpenAlexW4385834568

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.