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Record W3202261548 · doi:10.30683/1929-2279.2020.09.10

Advances in New Targets for Differentiation Therapy of Acute Myeloid Leukemia

2020· article· en· W3202261548 on OpenAlexvenueno aff
Jingfang Yao, Mengjie Zhao, Jiangyun Wang, Liuya Wei

Bibliographic record

VenueJournal of cancer research updates · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMyeloid leukemiaIsocitrate dehydrogenaseDiseaseCancer researchIDH1MyeloidLeukemiaBiologyTargeted therapyIDH2HomeoboxLineage (genetic)GeneMedicineBioinformaticsImmunologyGeneticsInternal medicineCancerEnzymeTranscription factorBiochemistryMutation

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) is a clinical and genetic heterogeneous disease with a poor prognosis. Recent advances in genomics and molecular biology have immensely improved the understanding of disease. The advantages of syndrome differentiation and treatment are strong selectivity, good curative effect and lesser side effects. In recent years, according to the molecular mechanism of acute myeloid leukemia, many new therapeutic targets have been found. New targets of differentiation therapy in recent years, such as cell cyclin-dependent kinase (CDK2), isocitrate dehydrogenase (IDH1, IDH2), Homeobox genes (HoxA9), Dihy-droorotate dehydrogenase (DHODH) and some others, are reviewed in this article.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.291
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.418
Teacher spread0.358 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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