标题： 一种用于体外多电极阵列记录分类的深度学习框架 显示英文标题

标题： A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings

摘要： 多电极阵列（MEAs）已被广泛用于记录神经元活动，这可用于基因缺陷和药物效果的诊断。 在本文中，我们解决了从不同基因型的小鼠和人类神经元培养物中对体外MEA记录进行分类的问题，其中没有简单的方法可以直接将原始序列作为输入来训练端到端分类模型。 虽然仔细手动提取一些特征可以部分解决问题，但这种方法存在明显的缺点，例如难以推广。 我们提出了一种深度学习框架来解决这个挑战。 我们的方法正确分类了来自两种不同基因型的神经元培养数据——一种是delta-catenin基因敲除的小鼠和来自威廉姆斯综合征的人类诱导多能干细胞衍生神经元。 通过将长记录分割成短片段进行训练，并在测试期间应用共识预测，我们的深度学习方法相比基于特征的逻辑回归，在小鼠MEA记录的预测准确率提高了16.69%。 我们在一组小鼠MEA记录数据中进一步实现了95.91%的准确率，这些数据全部是在体外六天记录的。 随着高密度MEA记录的日益普及，该方法可以推广用于携带不同突变的神经元分类和药物反应分类。 显示英文摘要

摘要： Multi-Electrode Arrays (MEAs) have been widely used to record neuronal activities, which could be used in the diagnosis of gene defects and drug effects. In this paper, we address the problem of classifying in vitro MEA recordings of mouse and human neuronal cultures from different genotypes, where there is no easy way to directly utilize raw sequences as inputs to train an end-to-end classification model. While carefully extracting some features by hand could partially solve the problem, this approach suffers from obvious drawbacks such as difficulty of generalizing. We propose a deep learning framework to address this challenge. Our approach correctly classifies neuronal culture data prepared from two different genotypes -- a mouse Knockout of the delta-catenin gene and human induced Pluripotent Stem Cell-derived neurons from Williams syndrome. By splitting the long recordings into short slices for training, and applying Consensus Prediction during testing, our deep learning approach improves the prediction accuracy by 16.69% compared with feature based Logistic Regression for mouse MEA recordings. We further achieve an accuracy of 95.91% using Consensus Prediction in one subset of mouse MEA recording data, which were all recorded at six days in vitro. As high-density MEA recordings become more widely available, this approach could be generalized for classification of neurons carrying different mutations and classification of drug responses.