Python 代码实现了一个基于图卷积网络（GCN）和模型无关元学习（MAML）的模型，用于预测 circRNA 和疾病之间的关联

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset, Subset
from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, average_precision_score, recall_score
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd
from imblearn.over_sampling import RandomOverSampler

# 1. 数据集类定义
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class CircrnaDataset(Dataset):
    def __init__(self, circ_sim_path, disease_sim_path, assoc_path):
        self.circ_sim = pd.read_csv(circ_sim_path, index_col=0).values
        self.disease_sim = pd.read_csv(disease_sim_path, index_col=0).values
        self.association = pd.read_csv(assoc_path, index_col=0).values

        assert self.association.shape == (self.circ_sim.shape[0], self.disease_sim.shape[0]), \
            f"关联矩阵的大小 {
     self.association.shape} 与 circRNA 和疾病矩阵 {
     self.circ_sim.shape}, {
     self.disease_sim.shape} 不匹配"

        circ_features = np.eye(self.circ_sim.shape[0])
        disease_features = np.eye(self.disease_sim.shape[0])
        disease_features = np.pad(disease_features, ((0, 0), (0, circ_features.shape[1] - disease_features.shape[1])), mode='constant')

        self.circ_features = circ_features
        self.disease_features = disease_features
        self.labels = self.association.flatten()

        self.features, self.labels = self.balance_data(self.circ_features, self.disease_features, self.labels)

    def balance_data(self, circ_features, disease_features, labels):
        features = []
        for i in range(circ_features.shape[0]):
            for j in range(disease_features