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: 7920529
• 负责人: HEIDI SPRATT
• 金额: $ 10.8万
• 依托单位: UNIVERSITY OF TEXAS MED BR GALVESTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7920529
• 关键词: Advanced Development; African American; Algorithms; Area; Area Under Curve; Biological; Biological Markers; Biological Neural Networks; Caliber; Cancer Patient; Caucasians; Caucasoid Race; Cessation of life; Chronic; 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; General Population; Goals; Grant; Hepatitis C; Hepatitis C virus; Hispanics; Incidence; Individual; Laboratories; Lasers; Lead; Liver; Liver Cirrhosis; Liver Fibrosis; Liver diseases; 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; experience; flexibility; improved; mathematical model; 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 general population is growing significantly which is of great concern. The progression of the viral infection as well as 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. 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 metabonomic 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 infected patients. I will use classical methods of data analysis such as principal components analysis, hierarchical clustering, neural networks, and logistic regression to create a first order biomarker panel. In phase two, I will utilize more sophisticated machine learning techniques such as kernel methods and support vector machines (SVM). My 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 the 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 versus non-cancer patients with HCV from applications of SVMs.

描述（由申请人提供）：丙型肝炎病毒（HCV）感染在普通人群中的发病率正在显着增长，这一点值得高度关注。病毒感染的进展以及治疗方式很大程度上取决于患者的纤维化阶段。因此，如果我们要制定适当的治疗方案，我们需要能够清楚地区分与 HCV 感染相关的肝纤维化的五个阶段。人们对晚期纤维化患者的 HCV 感染如何发展为肝癌知之甚少，因此该项目的一个重要目标是发现用于检测早期肝癌的生物标志物。将比较与 HCV 感染相关的纤维化的不同阶段，以发现哪些蛋白质和代谢物的表达存在差异；其目标是开发用于确定纤维化阶段的生物标志物组。此外，还将检查患者对治疗的蛋白质组和代谢组反应，以确定哪些个体将对治疗反应良好。肝癌患者还将与丙型肝炎病毒感染患者进行比较，以开发用于检测早期肝癌的生物标志物组。为了开发此类生物标志物，将对感染患者的血清和尿液样本进行表面增强激光解吸/电离和代谢组学实验。我将使用经典的数据分析方法（例如主成分分析、层次聚类、神经网络和逻辑回归）来创建一阶生物标记物面板。在第二阶段，我将利用更复杂的机器学习技术，例如核方法和支持向量机（SVM）。我的研究将特别关注 SVM 的进展，以创建针对 HCV 感染和疾病进展的高质量生物标志物组。具体目标是1）使用经典技术分析HCV数据，以确定在纤维化各个阶段差异表达的蛋白质和代谢物； 2）使用SVM分析数据，以更准确地对纤维化阶段进行分类； 3）改进SVM方法学，以获得更可靠的肝纤维化阶段诊断； 4) 使用改进的 SVM 技术分析患者对治疗的反应，以确定哪些患者更有可能对治疗产生反应； 5) 通过 SVM 的应用开发区分患有 HCV 的癌症患者和非癌症患者的标志物。