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227 Kinome Profiling of Bovine Leukemia Virus-induced Ovine Leukemia: An Approach for Identifying Altered Signaling Pathways and Drugable Targets in Cancer

2011· article· en· W2328660396 on OpenAlexaff
Anne Van den Broeke, Ryan J. Arsenault, Y. Cleuter, Céline Dehouck, Philippe Martiat, Arsène Burny, Scott Napper, Philip Griebel

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsKinomeLeukemiaBiologyCancer researchCancerPhosphorylationCell signalingCancer cellSignal transductionComputational biologyCellCell biologyImmunologyGenetics

Abstract

fetched live from OpenAlex

Screening new therapeutic agents which specifically inhibit phosphorylation of key cell signaling molecules has become a major focus in cancer research. It is critical, however, that the animal model or cell system used to screen therapeutic agents accurately reflect the biology of the target cancer cells. With the development of species-specific kinome arrays and their application in the Bovine Leukemia Virus (BLV) ovine leukemia model, it was possible to analyze changes in kinase activities associated with B cell transformation. Removal of cancer cells from the host and passage in tissue culture significantly altered phosphorylation patterns that define transformation of this specific cell lineage, suggesting that cell signaling closely reflects responses to the external environment. Thus, high-throughput kinome analysis in this unique large animal model of leukemogenesis provides an opportunity to identify critical phosphorylation events governing onset and progression of malignancy and define whether key cell signaling events that characterize primary cancer cells are accurately reflected in in vivo or in vitro screening models.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.070
GPT teacher head0.277
Teacher spread0.207 · 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.

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
Published2011
Admission routes1
Has abstractyes

Explore more

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