A model to classify the lifespan of Drosophila, the fruit fly, into short‐ and long‐lived categories based on a sleep characteristic, extracted from activity data, is developed using a two‐stage process. Stage 1 models the per‐minute activity counts of each fly using a zero‐inflated autoregressive conditional Poisson model. These probabilities are allowed to vary hourly, reflecting the circadian and other cycles present in a fly's sleep architecture. A 5‐day moving window is used to model data allowing the model parameters to vary over the course of the fly's life. The resulting probabilities capture information about changes in sleep patterns with age and are hypothesized to contain features that help categorize flies into short‐ and long‐lived groups. The resulting hourly zero‐inflation probabilities over a 24‐day period are utilized to create a ‘heat map’ containing information on the 24‐hour daily sleep cycle and its changes across the 24‐day observation period. In Stage 2, the heat maps for individual flies are used as inputs to a convolutional neural network to build a classification model. The estimated model provides a reasonably accurate way to group flies into lifespan categories. Grouping flies into such categories would facilitate the discovery of biochemical markers of aging.

中文翻译：

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预测果蝇的寿命：卷积神经网络和零膨胀自回归条件泊松模型的新应用

果蝇寿命分类模型，从活动数据中提取的果蝇根据睡眠特性分为短期和长期类别，是通过两步过程开发的。第1阶段使用零膨胀自回归条件泊松模型对每只苍蝇的每分钟活动计数进行建模。允许这些概率每小时变化一次，反映出苍蝇的睡眠结构中存在的昼夜节律和其他周期。使用一个为期5天的移动窗口来对数据进行建模，从而使模型参数可以在苍蝇的整个生命周期中变化。由此产生的概率捕获了有关随年龄变化的睡眠模式的信息，并被假定为包含有助于将苍蝇分为短寿命和长寿命组的特征。在24天的时间内每小时产生的零通货膨胀率被用于创建一个“热图”，其中包含有关24小时每天睡眠周期及其在24天观察期内的变化的信息。在阶段2中，将各个果蝇的热图用作卷积神经网络的输入，以建立分类模型。估计的模型提供了一种合理准确的方法，将果蝇分为寿命类别。将果蝇归为此类类别将有助于发现衰老的生化标记。