基于动态网络标记物和深度学习的小鼠肝癌潜伏期预警

人类战胜复杂疾病的关键在于早发现早治疗，常规的生化指标无法用于疾病潜伏期的检测，因为潜伏期的异常不是分子表达量的改变，而是分子间相关性的改变。为获得有关人体内机制和疾病的信息，动物模型的建立是研究肝癌发病生物学过程的一个极为重要的实验方法和手段，有助于更方便、更有效地认识人类疾病的发生、发展规律和研究防治措施。在本课题中我们构建了一个高效的肝癌诱导动物模型，可以在病因学（病因机制）和表型（体征和症状）方面与人类肝癌发病机制相当，使得以肝癌为代表的复杂的人类疾病在一个简化的系统中得到更好的理解。动物模型转录组学数据通过动态网络标记物 (DNB) 计算分子间相关性的突然改变，实现了潜伏期有效预警。在此基础上我们引入图卷积神经网络 (GCN) 深度学习算法，初步构建了一个简单的 DNB - GCN 复合预警系统。实现了对所构建的肝癌动物模型的疾病状态的判断，为后期优化 DNB - GCN 模型的预警能力（更准确更及时）、丰富其预报内容（计算出被检者在被测时处于潜伏期各阶段的概率）提供前期基础。本课题通过构建动物模型和样本高通量 DNB - GCN 理论实践，有助于后期个性化精准医疗的实现，具有一定的科学意义和社会价值。

The key to human victory over complex diseases lies in early detection and early treatment, and conventional biochemical indicators cannot be used to detect the incubation period of the disease, because the abnormality of the incubation period is not a change in molecular expression, but a change in the correlation between molecules. In order to obtain information about the mechanisms and diseases in the human body, the establishment of animal models is an extremely important experimental method and means for studying the biological process of liver cancer pathogenesis, which is conducive to more convenient and effective understanding of the occurrence, development and prevention and control measures of human diseases. In this project, we have constructed a highly efficient animal model of liver cancer induction, which can be comparable to the pathogenesis of human liver cancer in terms of etiology (etiological mechanism) and phenotype (signs and symptoms), so that complex human diseases represented by liver cancer can be better understood in a simplified system. The transcriptomics data of animal models were used to calculate sudden changes in intermolecular correlations through dynamic network biomarkers (DNBs) to achieve effective early warning of incubation periods. Base on that we introduce the graph convolutional neural network (GCN) deep learning algorithm to initially build a simple DNB - GCN composite early warning system. The judgment of the disease state of the constructed animal model of liver cancer is realized, which provides an early basis for optimizing the early warning ability of the DNB - GCN model in the later stage (more accurate and timely) and enriching its forecast content (calculating the probability that the subject is in each stage of the incubation period at the time of detection). Through the theoretical practice of constructing animal models and high-throughput DNB - GCN of samples, this project contributes to the realization of personalized precision medicine in the later stage, which has certain scientific significance and social value.

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