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Submitted thesis 03/02/2021 - now has corrections - resubmission deadline Nov-2022

提交论文 03/02/2021 - 现在已更正 - 重新提交截止日期 2022 年 11 月

基本信息

批准号：
2227769
负责人：
金额：
--
依托单位：
University College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2227769
关键词：
Submitted thesis 03 02 2021

项目摘要

Submitted thesis 03/02/2021 - now has corrections - resubmission deadline Nov-2022The information needed for the discovery of a new indication of an old drug (repurposing) might come from different sources. Electronic health records and databases of drugs adverse events will give information about "real life" use of drugs. GWAS will provide information on gene-phenotype association. From biology we might know what code what proteins, and some of the biological pathways in which these proteins participate. From experiments we know to what proteins each molecule (drug) attaches (or might attach to). All these pieces of information are stored in different databases, and often the data is not linkable, in the sense that we do not have the information for the same individual in all the databases.We will extract information on relevant associations and combine this information in such a way that we can provide a measurable degree of evidence or "belief" on the potential effect of a drug on a particular disease (i.e. provide evidence on a particular connection of disease-gene-protein-drug). The key problem here is to define a way of representing the degree of evidence or belief in each of the pieces of information, and come up with logically consistent rules to combine them.Grant 552247 replaces old CHAPTER grant 513157

提交论文03/02/2021 -现在有更正-重新提交截止日期2022年11月发现旧药物的新适应症（重新利用）所需的信息可能来自不同的来源。电子健康记录和药物不良事件数据库将提供有关“真实的生活”药物使用的信息。GWAS将提供有关基因-表型关联的信息。从生物学上，我们可能知道什么编码什么蛋白质，以及这些蛋白质参与的一些生物途径。从实验中我们知道每个分子（药物）附着（或可能附着）的蛋白质。所有这些信息都存储在不同的数据库中，而且数据通常不会被存储，我们将提取相关关联的信息，并将这些信息以某种方式进行联合收割机组合，以便我们能够提供某种药物对特定疾病的潜在效果的可衡量程度的证据或“信念”（即提供关于疾病-基因-蛋白质-药物的特定联系的证据）。这里的关键问题是定义一种方法来表示每条信息中的证据或信念的程度，并提出逻辑上一致的规则来联合收割机它们。