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Record W2009427752 · doi:10.1007/s10555-013-9429-5

The detection and implication of genome instability in cancer

2013· review· en· W2009427752 on OpenAlexafffund
Larissa A. Pikor, Kelsie L. Thu, Emily A. Vucic, Wan L. Lam

Bibliographic record

VenueCancer and Metastasis Reviews · 2013
Typereview
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of British ColumbiaOccupational Cancer Research Centre
FundersCongressionally Directed Medical Research ProgramsNational Cancer InstituteCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchCanary Foundation
KeywordsGenome instabilityMicrosatellite instabilityCancerCarcinogenesisBiologyInstabilityChromosome instabilityGenomeComputational biologyGenomicsGeneticsCancer researchGeneDNAMicrosatelliteDNA damage

Abstract

fetched live from OpenAlex

Genomic instability is a hallmark of cancer that leads to an increase in genetic alterations, thus enabling the acquisition of additional capabilities required for tumorigenesis and progression. Substantial heterogeneity in the amount and type of instability (nucleotide, microsatellite, or chromosomal) exists both within and between cancer types, with epithelial tumors typically displaying a greater degree of instability than hematological cancers. While high-throughput sequencing studies offer a comprehensive record of the genetic alterations within a tumor, detecting the rate of instability or cell-to-cell viability using this and most other available methods remains a challenge. Here, we discuss the different levels of genomic instability occurring in human cancers and touch on the current methods and limitations of detecting instability. We have applied one such approach to the surveying of public tumor data to provide a cursory view of genome instability across numerous tumor types.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.117
GPT teacher head0.403
Teacher spread0.285 · 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

Citations204
Published2013
Admission routes2
Has abstractyes

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