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

使用表观遗传学启发的联结主义模型为人类内脏利曼病建模提供透明度

基本信息

批准号：
EP/S003207/1
负责人：
Alexander Turner
金额：
$ 11.56万
依托单位：
University of Hull
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS003207%2F1
关键词：
Using Epigenetically Inspired Connectionist Models

项目摘要

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.

受生物启发的联结主义模型由多个相互连接的单元组成，这些单元旨在模拟自然界中产生涌现现象的生物过程。通常，连接主义模型用作能够通过示例学习的计算工具，例如通过学习前几个月的数据来预测证券交易所未来几天的活动。表观遗传启发的联结模型（Epigenetically inspired connectionist models，EICMs）是一种特殊类型的生物启发的联结模型，它允许在解决任务时激活和去激活其相互连接的单元。这些模型已被证明可以自主地将复杂的任务分解为更小的子任务，某些互连单元应用于某些子任务，而其他互连单元应用于其他子任务。一般来说，受生物学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联系的单位的一个紧急属性，很难解释为什么会做出特定的决定。因此，从他们做出的决定中获得信心是困难的。在决策过程中有信心是很重要的，特别是当他们被应用到被认为是“高风险”的领域，如医疗模拟和财务预测的任务。为了解决这些问题，这项工作的目的是开发一套技术，允许EICM提供一个理由，他们的决策过程中，基本上使其决策透明。这将通过分析模型分解复杂任务的方式来实现，它的哪些单元在任何给定时间都是活动的，然后将其与网络和任务的行为相关联。我们应用EICMs和本项目开发的技术来提高对人类内脏利什曼病（HVL）这一常见致命疾病的认识。对HVL的免疫应答是患者结局的重要指标，是多种相互作用的细胞、巨噬细胞和特异性细胞因子应答之间相互作用的产物。项目合作伙伴Simomics是一家世界领先的疾病建模公司，拥有一套全面的数据集，描述了不同时间尺度上HVL免疫反应的变化，并提供给本项目使用。HVL的整体发展和对其的免疫应答尚未完全了解。在这项工作中开发的技术，能够提供一个合理的决策过程，将被应用于学习这些过程之间的相互作用和相互作用。这将允许模型解释在HVL感染的持续时间内免疫应答中哪些过程最重要。通过对生物建模领域的贡献，其他领域将能够采用这项工作中开发的模型，以在其他领域提供透明度。