Using Epigenetically-Inspired Connectionist Models to Provide Transparency In The Modelling of Human Visceral Leismaniasis

受生物启发的连接主义模型是由多个相互连接的单元组成的，这些单元被设计来模拟自然界中产生紧急现象的生物过程。通常，连接主义模型被用作计算工具，例如，能够通过学习来学习，例如通过学习前几个月的数据来预测股票交易所的下一天活动。表观遗传启发的连接主义模型(EICM)是一种特殊类型的生物启发的连接主义模型，它允许在解决任务时激活和去激活它们相互连接的单元。这些模型已被证明可以自主地将复杂任务分解为较小的子任务，将某些相互连接的单元应用于某些子任务，而将其他相互连接的单元应用于其他子任务。一般来说，生物启发的联结主义模型很难解释。他们的决策过程是他们相互关联的单位的一个紧急性质，很难解释为什么做出了具体的决定。正因为如此，从他们做出的决定中获得信心是困难的。在决策过程中保持信心是很重要的，特别是当它们所应用的任务属于被认为是“高风险”的领域时，例如医学模拟和金融预测。为了解决这些问题，这项工作旨在开发一套技术，使EICM能够为其决策过程提供理由，本质上使其决策透明。这将通过分析模型分解复杂任务的方式来实现，其中哪些单元在任何给定时间处于活动状态，然后将其与网络和任务的行为关联起来。我们应用EICM和本项目中开发的技术来提高对通常致命的人类内脏利什曼病(HVL)的理解。对HVL的免疫反应是患者预后的一个重要指标，是多种相互作用的细胞、巨噬细胞和特定细胞因子反应相互作用的产物。项目合作伙伴世界领先的疾病建模公司Simonics拥有一个全面的数据集，该数据集描述了在不同时间尺度上相对于HVL的免疫反应的变化，并提供了该数据以供本项目使用。人类免疫缺陷病毒的整体发展和免疫反应还不是很清楚。这项工作中开发的能够为他们的决策过程提供理论基础的技术将被应用于学习这些过程之间的相互作用和相互作用。这将使模型能够解释在HVL感染持续时间内，哪些过程在免疫反应中最重要。生物建模非常强调结果的透明度和可信度，通过对这一领域的贡献，其他领域将能够采用在这项工作中开发的模型，以便在其他领域提供透明度。

Biologically inspired connectionist models are made up of multiple interconnected units which are designed to mimic biological processes in nature which give rise to emergent phenomena. Typically, connectionist models are used as computational tools which are capable of learning by example, for instance predicting the next days activity on the stock exchange by learning from previous months data. Epigenetically inspired connectionist models (EICMs) are a particular type of biologically inspired connectionist model which allow for the activation and deactivation of their interconnected units whist they are solving a task. These models have been shown to break complex tasks down into smaller sub-tasks autonomously, with certain interconnected units being applied to certain sub-tasks, and other interconnected units being applied to other sub-tasks. Biologically inspired connectionist models in general are difficult to interpret. Their decision making processes are an emergent property of their interconnected units, from which it is very difficult to provide an explanation as to why specific decisions have been made. Because of this, deriving confidence from the decisions they make is difficult. Having confidence in the decision making process is of importance especially when the tasks they are applied to are in domains which are considered "high risk" such as medical simulations and financial forecasting. To address these issues, this work aims to develop a set of techniques which allow for EICMs to provide a rationale for their decision making process, essentially making its decisions transparent. This will be achieved by analysing the way the model breaks down complex tasks, which of its units are active at any given time and then correlating this with the behaviour of both the network and the task. We apply the EICMs and the techniques developed in this project to improve the understanding of the often fatal disease human visceral leismaniasis (HVL). The immune response to HVL is a significant indicator of patient outcome and is the product of the interplay between multiple interacting cells, macrophages and specific cytokine responses. The project partner Simomics, a world leading disease modelling company, has a comprehensive data set which describes changes to the immune response in reference to HVL over varying timescales, and has provided it for use during this project. The overall development of HVL and the immune response to it is not well understood. The techniques developed in this work which are able to provide a rationale for their decision making process, will be applied to learn the interplay and interactions between these processes. This will allow for model to provide an explanation of what processes are most important in the immune response over the duration of HVL infection. By contributing to the field of biological modelling, which places a strong emphasis on transparency and confidence in results, other fields will be able to adopt the models developed in this work to provide transparency in other domains.