Additional file 1 of Rare deleterious mutations of HNRNP genes result in shared neurodevelopmental disorders
Bibliographic record
Abstract
Additional file 1: Supplementary Tables S1-S12. Table S1. Cohorts utilized in current study. Table S2. Pathogenicity predictions by gnomAD and GeVIR for each hnRNP. Table S3. Significant p values of two-way ANOVA between cell type and transcripts per million (TPM) for each NDD HNRNP’s GTEx data. Table S4. P values and fold change for scRNA data from developing human cortex. Table S5. Significant p values of two-way ANOVA between cell type and fold expression for each HNRNP. Table S6. De novo enrichment and CLUMP analyses of current cohort. Table S7. Phenotypes among HNRNP-related disorders. Table S8. Uncorrected p values for pairwise Fisher’s exact tests for each phenotype occurring in 20% of an HNRNP group. Table S9. Uncorrected p values for phenotype correlations between each HNRNP-related disorder. Table S10. Variants and phenotypes for HNRNPs with < 3 probands. Table S11. Probands with copy number variants. Table S12. Shared HPO terms with similarly presenting genetic disorders.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.529 | 0.094 |
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".