DNA甲基化数据预测自杀和非自杀死亡：一种机器学习方法
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach
摘要：
本研究的目的是使用现代机器学习算法从DNA甲基化数据预测自杀和非自杀死亡。我们使用支持向量机对现有的二级数据进行分类，这些数据由两个大脑皮层区域组织的甲基化DNA探针强度的归一化值组成，以区分自杀病例和对照病例。在分类之前，我们使用主成分分析（PCA）和t分布随机邻居嵌入（t-SNE）来降低数据的维数。与PCA相比，现代数据可视化方法t-SNE在降维方面表现更好。t-SNE解释了低维数据中可能的非线性模式。我们应用了四重交叉验证，其中从t-SNE得到的输出被用作支持向量机（SVM）的训练数据。尽管使用了交叉验证，BA 11数据对自杀死亡的名义上完美的预测表明模型可能过度拟合。这项研究也可能受到“光谱偏差”的影响，因为这些个体只在两种极端情况下进行研究。这项研究构成了一项基线研究，用于根据DNA甲基化数据对自杀和非自杀死亡进行分类。未来的研究样本量更大，同时可能纳入来自活体个体的甲基化数据，可能会减少偏倚并提高结果的准确性。
英文摘要：
The objective of this study is to predict suicidal and non-suicidal deaths from DNA methylation data using a modern machine learning algorithm. We used support vector machines to classify existing secondary data consisting of normalized values of methylated DNA probe intensities from tissues of two cortical brain regions to distinguish suicide cases from control cases. Before classification, we employed Principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimension of the data. In comparison to PCA, the modern data visualization method t-SNE performs better in dimensionality reduction. t-SNE accounts for the possible non-linear patterns in low-dimensional data. We applied four-fold cross-validation in which the resulting output from t-SNE was used as training data for the Support Vector Machine (SVM). Despite the use of cross-validation, the nominally perfect prediction of suicidal deaths for BA11 data suggests possible over-fitting of the model. The study also may have suffered from 'spectrum bias' since the individuals were only studied from two extreme scenarios. This research constitutes a baseline study for classifying suicidal and non-suicidal deaths from DNA methylation data. Future studies with larger sample size, while possibly incorporating methylation data from living individuals, may reduce the bias and improve the accuracy of the results.
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