Identification of therapeutically-relevant patient subgroups from clinical and biological data

作为一种疾病，脓毒症是重要的原因有很多。重症监护室（ICU）中约50%的入院率和每年大量住院费用（140亿美元/年）由重度脓毒症引起（Cawcutt和Peters，2014）。此外，脓毒症可导致脓毒性休克，即脓毒症伴低血压或高乳酸血症，这是一种可能致命的疾病（死亡率为24-32%）（Daviaud et al，2015）。尽管由于临床护理的改善，死亡率在过去几年中有所下降（Daviaud et al，2015），但在准确和及时诊断方面仍有很大的改进空间。该项目的目的是开发一种方法，通过该方法，临床医生可以识别由于特定入侵病原体感染而更容易发生脓毒症或脓毒性休克的患者。这种测试将使临床医生能够识别患者属于特定病原体的风险组，从而使他们能够给予适当的抗生素治疗这种病原体，并节省时间（因为会尽快给予最有效的治疗）和资源这将通过使用图论对患者的表达数据进行生物信息学分析来完成。将识别患者之间的关联，允许将其亚分类为具有不同可能性的组。python编程语言将用于进行此分析，该领域的工作将基于Baillie实验室先前进行的研究。

As a disease, sepsis is important for a multitude of reasons. Severe sepsis is responsible for approximately 50% of admission in the Intensive Care Unit (ICU) and significant annual hospitalisation costs ($14 billion/year) (Cawcutt and Peters, 2014). In addition, sepsis can lead to septic shock, which is sepsis with hypotension or hyperlactatemia, a condition which can be fatal (24-32% mortality rate) (Daviaud et al, 2015). Even though mortality rates have decreased over the past years due to improved clinical care (Daviaud et al, 2015), there is still much room for improvement in terms of accurate and timely diagnosis.The aim of the project is to develop a method by which clinicians might identify patients with greater predisposition to develop sepsis, or septic shock, as a result of infection by specific invading pathogens. This test will enable clinicians to identify a patient as belonging to a risk group for a particular pathogen, thus allowing them to administer the proper course of antibiotics to treat this pathogen and save both time (since the most effective treatment will be given as soon as possible) and resources (as initial use of broad spectrum antibiotics will be limited, reducing costs).This will be done by means of bioinformatic analysis of patient's expression data using graph theory. The associations between patients will be discerned, allowing for their sub-categorisation into groups of different susceptibilities. The python programming language will be used to conduct this analysis and the work on this area will be based on previous research conducted by the Baillie lab.