Classification of Schizophrenia Patients According to DNA Methylation Data Based on Meta-learning
Bibliographic record
Abstract
The question for understanding the correlation between genetic expression and diagnosis of schizophrenia patients has always been challenging. The DNA methylation can provide an effective approach to solve the problem. However, limited by the size of datasets and the high dimensionality, conventional feature selection and pattern recognition methods cause a severe overfitting phenomenon. In this paper, the DNA methylation data are analyzed to filter out the most important gene points for classification. To alleviate the negative overfitting impacts the optimization-based meta-learning i.e., the MAML algorithm, is developed to solve the multi-classification and feature selection problems. The proposed method combining MAML with attention-based mechanism is capable of conducting feature selection with fast generalization and quick adaptation for small samples. The major work lies in that the proposed model is pretrained with TCGA which can be tailored to our circumstance with priori information. To test the efficiency of the proposed algorithm, the DNA methylation data are used including homicidal schizophrenics and violent schizophrenics from some authoritative institute. Our method achieves a recognition accuracy of 91.89% (homicidal schizophrenics, schizophrenics without violent behaviors, and normal people) and 67.96% (schizophrenics with violent behaviors, schizophrenics without violent behaviors, and normal people}) respectively, which is a great improvement compared to the best-known results based on the traditional method with 82.52% and 63.76%. Furthermore, 1000 significant feature gene points are selected from more than 400 thousand feature points.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".