Robust Instrumental Variables estimators to boost protein biomarkers discoveries using gene expression data

生物标志物可以被定义为用于测量和评估疾病状态或对治疗的反应的指标。例如，低密度脂蛋白（LDL）胆固醇的血浆水平有助于监测患者的一般心血管健康。迄今为止，存在未满足的鉴定分子生物标志物（例如，蛋白质），包括癌症、心力衰竭和慢性阻塞性肺病。这些指标有助于开发最小侵入性和时效性的诊断工具，改善病人的护理，降低加拿大卫生系统的成本，能够测量血液中基因和蛋白质的新技术为这些生物医学调查开辟了新的途径。然而，需要新的统计方法来统计这些技术产生的信息。在过去，我一直致力于蛋白质组学数据的分析，旨在开发临床测试，可以取代活检来监测移植中的器官排斥反应。我提议的研究计划提供了适当的计算方法来寻找蛋白质生物标志物，而经典的统计方法可能无法检测到它们，这可以从相关的基因组表达中借用力量。新方法将彻底改变生物标志物的搜索方式。

Biomarkers can be defined as indicators used to measure and evaluate the state of a disease or a response to treatment. For example, plasma levels of low-density lipoprotein (LDL) cholesterol help to monitor patients' general cardiovascular health. To date, there is an unmet clinical need to identify molecular biomarkers (e.g., proteins) of various diseases, including cancer, heart failure, and chronic obstructive pulmonary disease. These indicators can yield to the development of minimally-invasive and time-effective diagnostic tools improving patients' care and decreasing costs in the Canadian health system.Novel technologies that can measure genes and proteins in the blood open new venues for these biomedical investigations. However, new statistical methods are needed to distil the information generated by these technologies. In the past, I have been working on the analysis of proteomics data aimed to develop clinical tests that can replace biopsies to monitor organ rejection in transplantation. My proposed research program offers appropriate computational approaches to find protein biomarkers, where classical statistical methods may fail to detect them, borrowing strength from related genomics expression. The new methodology will completely change the way that biomarkers are searched.