个性化疾病代谢分析
Personalized Metabolic Analysis of Diseases
摘要：
在包括癌症在内的许多疾病中，患者细胞的代谢线路发生了急剧变化。了解这些变化的性质可能为新的治疗机会以及为患者制定个性化治疗策略铺平道路。在本文中，我们提出了一种算法，代谢，它允许系统级分析疾病状态下细胞生化网络的变化。它能够在反应和途径水平粒度上研究疾病，以获得详细和概括的疾病病因学视图。Metabolitics采用具有目标函数的通量变异性分析，该目标函数基于生物流体代谢组学测量以个性化方式动态构建。此外，Metabolitics为每种疾病建立分类模型，以根据计算出的代谢网络变化诊断患者并预测其亚组。我们展示了代谢物在三种不同疾病上的应用，即乳腺癌、克罗恩病和结直肠癌。我们的研究结果表明，所构建的监督学习模型成功地诊断患者的f1分数平均超过90%。此外，除了证实先前报道的乳腺癌相关途径外，我们发现丁酸代谢活性显著降低，而精氨酸和脯氨酸代谢活性显著增加，这在以前没有报道过。Metabolitics在pypi中作为Python包提供。
英文摘要：
The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities, as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm, Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with an objective function which is dynamically built based on the biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds classification models for each disease to diagnose patients and predict their subgroups based on the computed metabolic network changes. We demonstrate the use of Metabolitics on three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully diagnose patients by f1-score of over 90% on the average. Besides, in addition to the confirmation of previously reported breast cancer associated pathways, we discovered that Butanoate metabolism experiences significantly decreased activity, while Arginine and Proline Metabolism is subject to significant increase in activity, which have not been reported before. Metabolitics is made available as a Python package in pypi.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
