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Record W4205392953 · doi:10.1002/bies.202100183

The complex web of canonical and non‐canonical Hedgehog signaling

2022· article· en· W4205392953 on OpenAlexafffund
Tara Akhshi, Rachel Shannon, William S. Trimble

Bibliographic record

VenueBioEssays · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSmoothenedNon canonicalHedgehogHedgehog signaling pathwayBiologySignal transductionFunction (biology)CiliumCell signalingCell biologyComputational biology

Abstract

fetched live from OpenAlex

Hedgehog (Hh) signaling is a widely studied signaling pathway because of its critical roles during development and in cell homeostasis. Vertebrate canonical and non-canonical Hh signaling are typically assumed to be distinct and occur in different cellular compartments. While research has primarily focused on the canonical form of Hh signaling and its dependency on primary cilia - microtubule-based signaling hubs - an extensive list of crucial functions mediated by non-canonical Hh signaling has emerged. Moreover, amounting evidence indicates that canonical and non-canonical modes of Hh signaling are interlinked, and that they can overlap spatially, and in many cases interact functionally. Here, we discuss some of the many cellular effects of non-canonical signaling and discuss new evidence indicating inter-relationships with canonical signaling. We discuss how Smoothened (Smo), a key component of the Hh pathway, might coordinate such diverse downstream effects. Collectively, pursuit of questions such as those proposed here will aid in elucidating the full extent of Smo function in development and advance its use as a target for cancer therapeutics.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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