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Record W2039256908 · doi:10.3389/fpls.2014.00709

Molecular locks and keys: the role of small molecules in phytohormone research

2014· review· en· W2039256908 on OpenAlexafffund
Sandra Fonseca, Abel Rosado, John Vaughan‐Hirsch, Anthony Bishopp, Andrea Chini

Bibliographic record

VenueFrontiers in Plant Science · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Parasitism and Resistance
Canadian institutionsUniversity of British Columbia
FundersAgencia Española de Cooperación Internacional para el DesarrolloBiotechnology and Biological Sciences Research CouncilMinisterio de Economía y CompetitividadNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsRoyal Society
KeywordsContext (archaeology)Chemical biologyBiologyComputational biologyFunction (biology)Plant hormoneChemical geneticsSmall moleculePlant biologyPlant developmentGenomicsHormoneAdaptation (eye)Cell biologyGeneticsNeuroscienceGeneBiochemistryGenomeBotany

Abstract

fetched live from OpenAlex

Plant adaptation, growth and development rely on the integration of many environmental and endogenous signals that collectively determine the overall plant phenotypic plasticity. Plant signaling molecules, also known as phytohormones, are fundamental to this process. These molecules act at low concentrations and regulate multiple aspects of plant fitness and development via complex signaling networks. By its nature, phytohormone research lies at the interface between chemistry and biology. Classically, the scientific community has always used synthetic phytohormones and analogs to study hormone functions and responses. However, recent advances in synthetic and combinational chemistry, have allowed a new field, plant chemical biology, to emerge and this has provided a powerful tool with which to study phytohormone function. Plant chemical biology is helping to address some of the most enduring questions in phytohormone research such as: Are there still undiscovered plant hormones? How can we identify novel signaling molecules? How can plants activate specific hormone responses in a tissue-specific manner? How can we modulate hormone responses in one developmental context without inducing detrimental effects on other processes? The chemical genomics approaches rely on the identification of small molecules modulating different biological processes and have recently identified active forms of plant hormones and molecules regulating many aspects of hormone synthesis, transport and response. We envision that the field of chemical genomics will continue to provide novel molecules able to elucidate specific aspects of hormone-mediated mechanisms. In addition, compounds blocking specific responses could uncover how complex biological responses are regulated. As we gain information about such compounds we can design small alterations to the chemical structure to further alter specificity, enhance affinity or modulate the activity of these compounds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.011
Scholarly communication0.0040.019
Open science0.0020.005
Research integrity0.0030.010
Insufficient payload (model declined to judge)0.0090.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.274
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2014
Admission routes2
Has abstractyes

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