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Record W4319790989 · doi:10.1002/ijch.202200088

Unraveling Complex MicroRNA Signaling Pathways with Activity‐Based Protein Profiling to Guide Therapeutic Discovery**

2023· article· en· W4319790989 on OpenAlexaff
Parrish Evers, John Paul Pezacki

Bibliographic record

VenueIsrael Journal of Chemistry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsmicroRNAComputational biologyEffectorChemistryRNACell biologyBiologyGeneBiochemistry

Abstract

fetched live from OpenAlex

Abstract microRNAs (miRNAs) are important cell factors that can play essential roles, such as regulating transcription factors in embryonic development. Traditionally, microarrays or high‐throughput sequencing of crosslinked protein‐RNA complexes have been applied to deduce direct targets, but these techniques fail to account for biomolecular interactions with functional consequences. Activity‐based protein profiling (ABPP) represents a promising alternative to conventional methods and was applied to the discovery of functional targets of human miRNAs. ABPP identified downregulation of MGLL and LIPC, in response to miR‐185 and miR‐27b, respectively. Hepatitis C virus (HCV) infection was found to be modulated by these indirect downstream effectors. These findings highlight the importance of identifying functional targets in developing novel ways to mimic or inhibit miRNA pathways. This review will focus on miRNA regulation and how ABPP can be used to study these pathways.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.269
Teacher spread0.245 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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