Classification of Schizophrenia Patients According to DNA Methylation Data Based on Meta-learning

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.