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Record W2093289166 · doi:10.1155/2013/612369

Mitochondria and Cancer: Past, Present, and Future

2013· review· en· W2093289166 on OpenAlex
Meghan L. Verschoor, Robert G. Ungard, Andrew Harbottle, John P. Jakupciak, Ryan Parr, Gurmit Singh

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

VenueBioMed Research International · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcMaster UniversityJuravinski Cancer Centre
Fundersnot available
KeywordsMitochondrionProteomicsBiologyComputational biologyGenomicsCancerBioinformaticsNeuroscienceData scienceGenomeGeneticsComputer scienceGene

Abstract

fetched live from OpenAlex

The area of mitochondrial genomics has undergone unprecedented growth over the past several years. With the advent of the age of omics, investigations have reached beyond the nucleus to encompass the close biological communication and finely coordinated interactions between mitochondria and their nuclear cell mate. Application of this holistic approach, to all metabolic interactions within the cell, is providing a more complete understanding of the molecular transformation of the cell from normal to malignant behavior, before histopathological indications are evident. In this review the surging momentum in mitochondrial science, as it relates to cancer, is described in three progressive perspectives: (1) Past: the historical contributions to current directions of research; (2) Present: Contemporary findings, results and approaches to mitochondria and cancer, including the role of next generation sequencing and proteomics; (3) FUTURE: Based on the present body of knowledge, the potential assets and benefits of mitochondrial research are projected into the near future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.713
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.087
GPT teacher head0.424
Teacher spread0.337 · 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