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Record W2598764473 · doi:10.1002/cbin.10770

MicroRNA: an important regulator in acute myeloid leukemia

2017· review· en· W2598764473 on OpenAlexaff
Xiaman Wang, Hongli Chen, Ju Bai, Aili He

Bibliographic record

VenueCell Biology International · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsmicroRNAMyeloid leukemiaBiologyHaematopoiesisCarcinogenesisRegulatorCancer researchLeukemiaEpigeneticsMyeloidBioinformaticsComputational biologyImmunologyGeneStem cellGenetics

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) are a general class of endogenous non-coding RNAs with a length of 22 nucleotides, widely existing in diverse species and playing important roles in malignancies initiation and progression. MiRNAs are essential to many in vivo biological processes such as cell proliferation, apoptosis, immune response, and tumorigenesis. Significant progress till date has been made in understanding the roles of microRNAs in normal hematopoiesis and hematopoietic malignant diseases. In this review, we summarize the particular signatures of microRNAs in acute myeloid leukemia (AML) patients with specific karyotype and the clinical significance of microRNAs in early diagnosis and treatment. MicroRNAs hypermethylation was also proved to correlate with the pathogenesis of AML. However, the target genes and exact pathways of microRNAs participating in these processes are still unknown and more efforts need to be made in the near future.

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: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.028
GPT teacher head0.336
Teacher spread0.309 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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