In this study, with the use of the information theory, we have proposed and proved a mathematical theorem by which we argue the reason for the existence of human diseases. To introduce our theoretical frame of reference, first, we put forward a modification of Shannon's entropy, computed for all available proteomes, as a tool to compare systems complexity and distinguish between the several levels of biological organizations. We establish a new approach to differentiate between several taxa and corroborate our findings through the latest tree of life. Furthermore, we found that human proteins with higher mutual information, derived from our theorem, are more prone to be involved in human diseases. We further discuss the dynamics of protein network stability and offer probable scenarios for the existence of human diseases and their varying occurrence rates. Moreover, we account for the reasoning behind our mathematical theorem and its biological inferences.

中文翻译：

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香农熵的扩展以解释分类群多样性和人类疾病

在这项研究中，利用信息论，我们提出并证明了一个数学定理，据此我们可以论证人类疾病存在的原因。为了介绍我们的理论参考框架，首先，我们对香农熵进行了修改，并针对所有可用的蛋白质组进行了计算，以此作为比较系统复杂性并区分生物学组织的几个层次的工具。我们建立了一种新的方法来区分几种分类，并通过最新的生命树来证实我们的发现。此外，我们发现从我们的定理得出的具有更高互信息的人类蛋白质更容易参与人类疾病。我们将进一步讨论蛋白质网络稳定性的动态变化，并为人类疾病的存在及其发生率的变化提供可能的方案。此外，我们解释了数学定理及其生物学推论背后的原因。