论文标题
多层感知器网络使用行为区分幼虫斑马鱼基因型
Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype using Behaviour
论文作者
论文摘要
斑马鱼是用于鉴定新疾病疗法的常见模型生物。通过观察治疗后的行为变化，可以在多孔板的幼虫斑马鱼上对幼虫斑马鱼进行高通量药物筛查。但是，由于获得的数据的高维度，对此行为的分析可能很困难。单个统计数据（例如行进距离）的统计分析通常不够强大，无法检测到治疗组之间有意义的差异。在这里，我们提出了一种通过基因型在5天大的基因型中对帕金森氏病的斑马鱼模型进行分类的方法。使用一组2D行为特征，我们训练一个多层感知器神经网络。我们进一步表明，使用集成梯度可以洞悉模型的每个行为特征对基因型分类的影响。通过这种方式，我们提供了一条新颖的管道，用于对斑马鱼幼虫进行分类，从特征制备开始，并以对所述特征的影响分析结束。
Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features.
