PANDA: Prioritization of autism‐genes using network‐based deep‐learning approach
Bibliographic record
Abstract
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of possibilities. Thus, computational methods have seen increasing applications in predicting gene-disease associations. We proposed a bioinformatics framework, Prioritization of Autism-genes using Network-based Deep-learning Approach (PANDA). Our approach aims to identify autism-genes across the human genome based on patterns of gene-gene interactions and topological similarity of genes in the interaction network. PANDA trains a graph deep learning classifier using the input of the human molecular interaction network and predicts and ranks the probability of autism association of every node (gene) in the network. PANDA was able to achieve a high classification accuracy of 89%, outperforming three other commonly used machine learning algorithms. Moreover, the gene prioritization ranking list produced by PANDA was evaluated and validated using an independent large-scale exome-sequencing study. The top 10% of PANDA-ranked genes were found significantly enriched for autism association.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".