Evidence map›Paper›PMID 36501364›Full record

ArticlePlants (Basel, Switzerland)2022

Modeling of Flowering Time in

Maria Bavykina, Nadezhda Kostina, Cheng-Ruei Lee, Roland Schafleitner, Eric Bishop-von Wettberg, Sergey V Nuzhdin, Maria Samsonova, Vitaly Gursky, Konstantin Kozlov

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Maria BavykinaMathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Nadezhda KostinaMathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Cheng-Ruei LeeInstitute of Ecology and Evolutionary Biology, National Taiwan University, Taipei 106319, Taiwan.
Roland SchafleitnerWorld Vegetable Center, Tainan 74151, Taiwan.ORCID 0000-0003-3637-7603
Eric Bishop-von WettbergDepartment of Plant and Soil Science, Gund Institute for the Environment, University of Vermont, Burlington, VT 05405, USA.ORCID 0000-0002-2724-0317
Sergey V NuzhdinMathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Maria SamsonovaMathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Vitaly GurskyTheoretical Department, Ioffe Institute, 194021 Saint Petersburg, Russia.ORCID 0000-0003-4094-1983
Konstantin KozlovMathematical Biology and Bioinformatics Lab, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.ORCID 0000-0001-6765-236X

Funding

Ministry of Science and Higher Education of the Russian Federation 075-15-2021-1333
6 · The paper itself

Abstract

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.

Indexed as

artificial image objectsclimatic factorsconvolutional neural networkflowering timeGWASmung beanrandom forest

Identifiers

PMID36501364
PMCPMC9738219

What OpenQuestion holds

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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 OpenQuestion graph.