Wnt signaling networks in autism spectrum disorder and intellectual disability
Bibliographic record
Abstract
BACKGROUND: Genetic factors play a major role in the risk for neurodevelopmental disorders such as autism spectrum disorders (ASDs) and intellectual disability (ID). The underlying genetic factors have become better understood in recent years due to advancements in next generation sequencing. These studies have uncovered a vast number of genes that are impacted by different types of mutations (e.g., de novo, missense, truncation, copy number variations). ABSTRACT: Given the large volume of genetic data, analyzing each gene on its own is not a feasible approach and will take years to complete, let alone attempt to use the information to develop novel therapeutics. To make sense of independent genomic data, one approach is to determine whether multiple risk genes function in common signaling pathways that identify signaling "hubs" where risk genes converge. This approach has led to multiple pathways being implicated, such as synaptic signaling, chromatin remodeling, alternative splicing, and protein translation, among many others. In this review, we analyze recent and historical evidence indicating that multiple risk genes, including genes denoted as high-confidence and likely causal, are part of the Wingless (Wnt signaling) pathway. In the brain, Wnt signaling is an evolutionarily conserved pathway that plays an instrumental role in developing neural circuits and adult brain function. CONCLUSIONS: We will also review evidence that pharmacological therapies and genetic mouse models further identify abnormal Wnt signaling, particularly at the synapse, as being disrupted in ASDs and contributing to disease pathology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".