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Record W2039905887 · doi:10.1101/pdb.top069765

Transgenic Mouse Models—A Seminal Breakthrough in Oncogene Research

2013· review· en· W2039905887 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCold Spring Harbor Protocols · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsTransgeneGenetically modified mouseCarcinogenesisOncogeneGain of functionBiologyFunction (biology)SuppressorCancerMetastasisComputational biologyCancer researchCell biologyGenePhenotypeGeneticsCell cycle

Abstract

fetched live from OpenAlex

Transgenic mouse models are an integral part of modern cancer research, providing a versatile and powerful means of studying tumor initiation and progression, metastasis, and therapy. The present repertoire of these models is very diverse, with a wide range of strategies used to induce tumorigenesis by expressing dominant-acting oncogenes or disrupting the function of tumor-suppressor genes, often in a highly tissue-specific manner. Much of the current technology used in the creation and characterization of transgenic mouse models of cancer will be discussed in depth elsewhere. However, to gain a complete appreciation and understanding of these complex models, it is important to review the history of the field. Transgenic mouse models of cancer evolved as a new and, compared with the early cell-culture-based techniques, more physiologically relevant approach for studying the properties and transforming capacities of oncogenes. Here, we will describe early transgenic mouse models of cancer based on tissue-specific expression of oncogenes and discuss their impact on the development of this still rapidly growing field.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.209
GPT teacher head0.464
Teacher spread0.255 · 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