Biomarker Discovery for Hepatitis C Progression using Machine Learning Techniques

使用机器学习技术发现丙型肝炎进展的生物标志物

• 批准号: 8308938
• 负责人: HEIDI SPRATT
• 金额: $ 13.32万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8308938
• 关键词: Advanced Development; African American; Algorithms; Area; Area Under Curve; Biological; Biological Markers; Biological Neural Networks; Caliber; Caucasians; Caucasoid Race; Cessation of life; Chronic Hepatitis; Cirrhosis; Classification; Clinical; Communicable Diseases; Communities; Complex; Data; Data Analyses; Data Set; Data Sources; Detection; Development; Diagnostic; Disease; Disease Outcome; Disease Progression; Early Diagnosis; Educational process of instructing; Enrollment; Ethnic Origin; Extramural Activities; Fibrosis; Funding; Future; Goals; Grant; Hepatitis C; Hepatitis C virus; Hispanics; Incidence; Individual; Laboratories; Lasers; Lead; Liver; Liver Cirrhosis; Liver Fibrosis; Logistic Regressions; Machine Learning; Malignant Neoplasms; Malignant neoplasm of liver; Measures; Methodology; Methods; Modality; Outcome; Patients; Pattern; Persons; Phase; Play; Population; Population Heterogeneity; Principal Component Analysis; Proteins; Proteomics; Public Health; Race; Receiver Operating Characteristics; Research; Research Personnel; Role; Sampling; Serum; Staging; Surface; Symptoms; Techniques; Testing; Therapeutic; Time; Training; United States; Urine; Validation; Virus; Virus Diseases; base; career development; chronic liver disease; experience; flexibility; improved; mathematical model; metabolomics; mortality; novel; outcome forecast; protein metabolite; racial difference; research study; response

DESCRIPTION (provided by applicant): The incidence of Hepatitis C Virus (HCV) infection in the Hispanic population is growing significantly which is of great concern. In Hispanics, the viral infection progresses more rapidly when compared with Caucasian patients and the treatment modality critically depends on the patient's stage of fibrosis. Thus we need to be able to clearly distinguish between the five stages of liver fibrosis associated with HCV infection if we are to prescribe the proper treatment. Disturbingly, current treatments seem to be less effective on Hispanics. Little is known about how HCV infection progresses to liver cancer in patients with advanced fibrosis, so one important goal of this project is to discover biomarkers for the detection of early stage liver cancer. The different stages of fibrosis associated with HCV infection will be compared to discover which proteins and metabolites are differentially expressed; the goal of which will be the development of a biomarker panel for fibrosis stage determination. Also, patient's proteomic and metabolomic responses to therapy will be examined to determine a priori which individuals will respond well to therapy. Patients with liver cancer will also be compared to HCV infected patients to develop a biomarker panel for the detection of early stage liver cancer. For the development of such biomarkers, Surface Enhanced Laser Desorption/ lonization and metabonomics experiments will be conducted on serum and urine samples from patients. We will use classical methods of data analysis such as principal component analysis, hierarchical clustering, neural networks, and logistic regression to create our first order biomarker panels. In phase two, we will utilize more sophisticated machine learning techniques such as kernel methods and support vector machines (SVM). Dr. Spratt's research will especially focus on making advances in SVMs to create high quality biomarker panels for HCV infection and disease progression. The specific aims are to 1) analyze the HCV data using classical techniques to identify proteins and metabolites which are differentially expressed in the various stages of fibrosis; 2) analyze the data using SVMs to more accurately classify the stage of fibrosis; 3) improve our SVM methodology to obtain a more reliable diagnostic of liver fibrosis stage; 4) analyze patient response to treatment using improved SVM techniques to determine which patients are more likely to respond to therapy; and 5) develop markers that distinguish cancer vs non-cancer patients with HCV from applications of SVMs.

描述（由申请人提供）：西班牙裔人群中丙型肝炎病毒（HCV）感染的发病率正在显着增长，这引起了人们的关注。在西班牙裔中，与高加索患者相比，病毒感染的进展速度更快，并且严重的治疗方式取决于患者的纤维化阶段。因此，如果我们要开出适当的治疗，我们需要能够清楚区分与HCV感染相关的肝纤维化的五个阶段。令人不安的是，目前的治疗方法似乎对西班牙裔效果不佳。对于晚期纤维化患者的HCV感染如何发展为肝癌，该项目的一个重要目标是发现生物标志物以发现早期肝癌的生物标志物。将与HCV感染相关的纤维化的不同阶段将与发现哪些蛋白质和代谢产物差异表达。其目标将是开发用于纤维化阶段测定的生物标志物面板。同样，将检查患者对治疗的蛋白质组学和代谢组反应，以确定个人对治疗的反应良好的先验。还将将肝癌患者与HCV感染患者进行比较，以开发生物标志物面板以检测早期肝癌。为了开发此类生物标志物，将对患者的血清和尿液样本进行表面增强的激光解吸/孤独和替代实验。我们将使用经典的数据分析方法，例如主成分分析，分层聚类，神经网络和逻辑回归来创建我们的一阶生物标志物面板。在第二阶段，我们将利用更复杂的机器学习技术，例如内核方法和支持向量机（SVM）。 Spratt博士的研究将特别集中于在SVM中进步，以创建用于HCV感染和疾病进展的高质量生物标志物。具体目的是1）使用经典技术分析HCV数据，以鉴定在纤维化的各个阶段差异表达的蛋白质和代谢产物； 2）使用SVM分析数据，以更准确地对纤维化分类； 3）改进我们的SVM方法，以获得更可靠的肝纤维化阶段诊断； 4）使用改进的SVM技术来分析患者对治疗的反应，以确定哪些患者更有可能对治疗反应； 5）开发标记，将癌症与HCV的非癌症患者与SVM的应用区分开。