Function Shapes Content: DNA-Methylation Marker Genes and their Impact for Molecular Mechanisms of Glioma
Bibliographic record
Abstract
Glioma is a clinically and biologically diverse disease. It challenges diagnosis and prognosis due to its molecular heterogeneity and diverse regimes of biological dysfunctions which are driven by genetic and epigenetic mechanisms. We discover the functional impact of sets of DNA methylation marker genes in the context of brain cancer subtypes as an exemplary approach how bioinformatics and particularly machine learning using self organizing maps (SOM) complements modern high-throughput genomic technologies. DNA methylation changes in gliomas comprise both, hyper- and hypomethylation in a subtype specific fashion. We compared pediatric (2 subtypes) and adult (4) glioblastoma and non-neoplastic brain. The functional impact of differential methylation marker sets is discovered in terms of gene set analysis which comprises a large collection of markers related to biological processes, literature data on gliomas and also chromatin states of the healthy brain. DNA methylation signature genes from alternative studies well agree with our signatures. SOM mapping of gene sets robustly identifies similarities between different marker sets even under conditions of noisy compositions. Mapping of previous sets of glioma markers reveals high redundancy and mixtures of subtypes in the reference cohorts. Consideration of the regulatory level of DNA methylation is inevitable for understanding cancer genesis and progression. It provides suited markers for diagnosis of glioma subtypes and disentangles tumor heterogeneity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".