Activity-Dependent Neuroprotective Protein (ADNP): A Case Study for Highly Conserved Chordata-Specific Genes Shaping the Brain and Mutated in Cancer
Bibliographic record
Abstract
The recent finding of activity-dependent neuroprotective protein (ADNP) as a protein decreased in serum of patients with Alzheimer's disease (AD) compared to controls, alongside with the discovery of ADNP mutations in autism and coupled with the original description of cancer mutations, ignited an interest for a comparative analysis of ADNP with other AD/autism/cancer-associated genes. We strive toward a better understanding of the molecular structure of key players in psychiatric/neurodegenerative diseases including autism, schizophrenia, and AD. This article includes data mining and bioinformatics analysis on the ADNP gene and protein, in addition to other related genes, with emphasis on recent literature. ADNP is discovered here as unique to chordata with specific autism mutations different from cancer-associated mutation. Furthermore, ADNP exhibits similarities to other cancer/autism-associated genes. We suggest that key genes, which shape and maintain our brain and are prone to mutations, are by in large unique to chordata. Furthermore, these brain-controlling genes, like ADNP, are linked to cell growth and differentiation, and under different stress conditions may mutate or exhibit expression changes leading to cancer propagation. Better understanding of these genes could lead to better therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".