Bibliographic record
Abstract
Evolution in NeurogenomicsTo encourage further study of neurogenomics and disease by large scale DNA sequencing methods, we have selected five articles in our Research Topic entitled Evolution in Neurogenomics.This Research Topic includes neurodevelopmental disorders and disease, and approaches at the genomic, epigenomic, transcriptomic and epi-transcriptomic levels.In particular, Kim et al. explored clinical phenotypes and genetic variants by whole exome sequencing, a "massively parallel DNA-sequencing" method (Rabbani et al., 2014), in a cohort of pediatric patients with a diverse array of movement disorders.These are disorders that defy strict classification by conventional methodology.They successfully showed the potential of whole exome sequencing as a genetic diagnostic tool for yet another complex neurological disorder (Retterer et al., 2016).Our second selection of the Research Topic is Akter et al. who surveyed the clinically relevant "copy number variants" across a large number of patients, an underrepresented population in Bangladesh, with neurodevelopmental disorders.They sampled these variants by chromosomal microarray analysis and droplet digital polymerase chain reaction, an approach that is relatively less precise as a diagnostic tool, yet it showed applicability for clinical use and at a relatively low cost.For a more definable pathology, Hu et al. applied a meta-analytical method, including a large number of studies and an overall sample of over 18,000 individuals of Chinese ancestry, and identified three single nucleotide polymorphisms that show association with risk of ischemic stroke.The next two studies focused on glioma.For the first of these studies, Zhang et al. constructed and validated a risk score model for prognosis of low-grade gliomas.They based their analysis on 14 genes that are chromatin regulators, and included data from single-cell RNA-seq along with clinical data from The Cancer Genome Atlas.For the next study, Zhang et al. reported 12 m6A regulatory genes as putative biomarkers for the prognosis of glioma, a finding that included 1,600 samples.Both are disease association studies that include different methodologies, while the challenge is in synthesizing their findings for advancement of knowledge in neurology and biomedicine.Likewise, current research in neurogenomics is showing a great potential for association of genetic features with disease, including by genome-and transcriptome-based analyses of genetic variation, long-read RNA sequencing (Gao et al., 2023), and spatial/temporal in situ genome and transcriptome mapping (Longo et al., 2021;Payne et al., 2021).The studies of our Research Topic are exemplars of a heterogeneity of techniques for surveying genetic variation, such as by the use of microarray analysis or whole exome sequencing.Of special
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.026 |
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".