Evidence map›Paper›PMID 35300116›Full record

ArticleFrontiers in molecular biosciences2022

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model.

Ratklao Siriwach, Jun Matsuzaki, Takeshi Saito, Hiroshi Nishimura, Masahide Isozaki, Yosuke Isoyama, Muneo Sato, Masanori Arita, Shotaro Akaho, Tadahisa Higashide and 2 more

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In one paragraph

Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

12 authors at 6 institutions in 1 country.

Ratklao SiriwachRIKEN Center for Sustainable Resource Science, Yokohama, Japan.
Jun MatsuzakiRIKEN Center for Sustainable Resource Science, Yokohama, Japan.
Takeshi SaitoInstitute of Vegetable and Floriculture Science, NARO, Tsukuba, Japan.
Hiroshi NishimuraMie Prefecture Agricultural Research Institute, Matsusaka, Japan.
Masahide IsozakiMie Prefecture Agricultural Research Institute, Matsusaka, Japan.
Yosuke IsoyamaMie Prefecture Agricultural Research Institute, Matsusaka, Japan.
Muneo SatoRIKEN Center for Sustainable Resource Science, Yokohama, Japan.
Masanori AritaRIKEN Center for Sustainable Resource Science, Yokohama, Japan.
Shotaro AkahoNational Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.
Tadahisa HigashideInstitute of Vegetable and Floriculture Science, NARO, Tsukuba, Japan.
Kentaro YanoBioinformatics Laboratory, Department of Life Sciences, School of Agriculture, Meiji University, Kawasaki, Japan.
Masami Yokota HiraiRIKEN Center for Sustainable Resource Science, Yokohama, Japan.
RIKEN Center for Sustainable Resource Science · JPMie Agricultural Research Institute · JPInstitute of Vegetable and Floriculture Science · JPMeiji University · JPNational Institute of Advanced Industrial Science and Technology · JPNational Institute of Genetics · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R

Indexed as

anthesis rateLASSOmachine learningmetabolitesmetabolometomatotrigonelline

Identifiers

PMID35300116
PMCPMC8923526
OpenAlexW4214577245

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.