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Record W4393768785 · doi:10.5281/zenodo.7338400

Predictive modelling of brain metastasis risk and non-invasive biomarker detection using DNA methylation signatures

2022· dataset· en· W4393768785 on OpenAlexaff
Jeffrey Zuccato, Vikas Patil, Gelareh Zadeh

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDNA methylationBiomarkerBrain metastasisComputational biologyMethylationOncologyDNAMetastasisBiologyMedicineInternal medicineCancerGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

Methylated cell-free DNA was sequenced for 123 BM plasma and compared to plasma methylomes from 107 gliomas, central nervous system (CNS) lymphomas (CNSL), and non-CNS tumor controls. Plasma methylome-based classifiers of BM from other entities were built in fifty 80% discovery set iterations of 92/123 BM samples. External publicly-available tissue methylation data on 442 LUAD, 85 BM, and 146 glioma/CNSL/control samples were acquired for validation and the remaining 31/123 BM plasma samples were used for additional validation.

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)0.002
metaresearch head score (Gemma)0.003
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.053
GPT teacher head0.274
Teacher spread0.221 · 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
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicBrain Metastases and TreatmentFrench-language works237,207