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Record W2565124624 · doi:10.1158/1538-7445.am2015-2123

Abstract 2123: Protein synthesis and its control in cancer development, progression and treatment

2015· article· en· W2565124624 on OpenAlexaff
Armen Parsyan

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMcGill University
Fundersnot available
KeywordsTranslation (biology)CancerPI3K/AKT/mTOR pathwayEIF4EBiologyMAPK/ERK pathwayProtein biosynthesisSignal transductionCancer researchGeneticsMessenger RNAGene

Abstract

fetched live from OpenAlex

Abstract A central dogma of molecular biology posits that the protein biosynthetic pathway generally follows the three major events: replication, transcription and translation. The abnormal functioning of any of these processes can nurture malignant cellular transformation. While other components of the central dogma were front-page in cancer research, translation, somewhat, remained in the shades. This presentation is based on the recently published book “Translation and Its Regulation in Cancer Biology and Medicine”* that for the first time comprehensively summarizes and analyzes decades of information into the role of the aberrations in protein synthesis, translation, as well as its regulation in the biology of cancer. The mechanisms of action of various translation factors, such as oncoprotein eIF4E, and tumor suppressors, such as PDCD4, are intensively discussed. Other, less-studied protein factors participating in the complex process of translation are presented in light of their known or emerging roles in cancer development and progression. In addition, the presentation focuses on the oncogenic role of the regulation of the translation machinery by fundamental cellular signal transduction pathways, such as mTOR, MAPK and others. Finally, clinical applications of the current knowledge regarding the translation machinery, its function and regulation in cancer are highlighted, including the use of the translation factors as diagnostic and prognostic markers, as well as factors for novel approaches to targeted pharmacologic treatment. * Translation and Its Regulation in Cancer Biology and Medicine Parsyan, Armen (Ed.) 2014, XXXIV, 697 p. 52 illus., 44 illus. in color., Hardcover ISBN 978-94-017-9078-9 Springer Publishing Website: http://www.springer.com/biomed/cancer/book/978-94-017-9077-2 Citation Format: Armen Parsyan. Protein synthesis and its control in cancer development, progression and treatment. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2123. doi:10.1158/1538-7445.AM2015-2123

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0170.013

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 head0.085
GPT teacher head0.401
Teacher spread0.316 · 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
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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