Classifying addictions using machine learning analysis of multidimensional data

使用多维数据的机器学习分析对成瘾进行分类

• 批准号: 9224405
• 负责人: Jinbo Bi
• 金额: $ 16.35万
• 依托单位: UNIVERSITY OF CONNECTICUT STORRS
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9224405
• 关键词: Adherence; Aftercare; Alcohol dependence; Alcohols; Algorithms; Area; Behavioral; Biological; Biological Markers; Biosensor; Characteristics; Classification; Clinical; Cluster Analysis; Cocaine; Cocaine Dependence; Collaborations; Combined Modality Therapy; Complex; Computational Science; DSM-IV; DSM-V; Data; Data Analyses; Data Set; Development; Diagnosis; Diagnostic; Diagnostic and Statistical Manual of Mental Disorders; Dimensions; Disease; Drug Use Disorder; Electroencephalography; Etiology; Foundations; Functional Magnetic Resonance Imaging; Funding; Future; Genes; Genetic; Genetic Markers; Genetic study; Genomics; Genotype; Goals; Heritability; Heterogeneity; Independent Scientist Award; Individual; Interdisciplinary Study; Investigation; Joints; Knowledge; Machine Learning; Measurement; Measures; Methods; Modeling; National Institute of Drug Abuse; Neurobiology; Opiate Addiction; Opioid; Patients; Pattern; Pharmacogenetics; Pharmacotherapy; Phenotype; Population; Reading; Recording of previous events; Recruitment Activity; Research; Research Personnel; Risk Factors; Sampling; Scientist; Signs and Symptoms; Solid; Statistical Methods; Statistical Models; Subgroup; Substance Addiction; Substance Use Disorder; Surveys; Symptoms; Testing; Time; Training; Training Activity; Treatment outcome; Work; addiction; alcohol use disorder; biomedical informatics; career development; cocaine use; contingency management; design; disease classification; disorder subtype; endophenotype; genetic association; genomic data; imaging genetics; improved; innovation; neural correlate; novel; novel strategies; opioid use disorder; outcome prediction; personalized medicine; precision medicine; programs; secondary analysis; skills; social; tool; treatment planning; treatment response; tutoring

ABSTRACT This Independent Scientist Award will significantly enhance my research capabilities, enabling me to become a leading quantitative investigator in the field of substance use disorders (SUDs). Specifically, it will allow me to increase my knowledge in the areas of SUD phenotypes, treatment and genetics. SUDs are clinically and etiologically heterogeneous and their classification has been difficult. This application reflects my ongoing commitment to developing an innovative and interdisciplinary research program on the classification of SUDs through quantitative analysis of multidimensional data. My extensive training in computational science and prior research on biomedical informatics have provided me with the skills to design, implement and evaluate advanced algorithms and sophisticated analyses to solve challenging problems in classifying SUDs. My ongoing NIDA-funded R01 employs a large (n=~12,000) sample aggregated from multiple genetic studies of cocaine, opioid, and alcohol dependence to develop and evaluate novel statistical models to generate clinical SUD subtypes that are optimized for gene finding. This K02 proposal extends that work to evaluate treatment outcome in refined subgroups of SUD populations using data from treatment studies for cocaine, opioid, alcohol and multiple substance dependence. This project will integrate data from diagnostic behavioral variables and genotypes, as well as biological/neurobiological features of the disorders and repeated measures of treatment outcome. The primary career development goals of this application are to: (1) understand the reliability, validity and functional mechanisms of various phenotyping methods; (2) to continue training in the genetics of addictions; and (3) to gain greater knowledge of different treatment approaches and their efficacy. A solid foundation in these areas will enhance my ability to realize the full potential of the data collected and aggregated from multiple dimensions, and to use the data to design the most clinically useful analysis and generate innovative solutions to diagnostic and predictive challenges in SUD research. Through formal coursework, directed readings, individual tutoring and intensive multidisciplinary collaboration with a diverse team of world-renowned researchers, I will receive training and collect pilot data for future R01 projects by examining (Aim I): whether clinically-defined highly heritable subtypes derived in my current R01 project predict differential treatment response; (Aim II) whether new statistical models that directly combine treatment data with behavioral, biological, and genomic data identify refined subtypes with confirmatory multilevel evidence; and (Aim III) whether there are genetic and social moderators of treatment outcome by subtype. The overall goal of this proposal is to further my independent and multidisciplinary research program in the development of statistical methods for refined classification of SUDs. The K02 award will provide me with the protected time necessary to fully engage in the training activities described that will enhance my knowledge and skills to enable me to make important, novel contributions to the genetics and treatment of SUD.

摘要 这个独立科学家奖将大大增强我的研究能力，使我成为一名 物质使用障碍(SUDS)领域的领先定量研究人员。具体地说，它将允许我 增加我在sud表型、治疗和遗传学方面的知识。肥皂泡在临床上是 病原学上的异质性，它们的分类一直很困难。此应用程序反映了我正在进行的 致力于开发关于肥皂泡分类的创新和跨学科研究方案 通过对多维数据的定量分析。我受过广泛的计算科学和 以前对生物医学信息学的研究为我提供了设计、实施和评估的技能 先进的算法和复杂的分析，以解决在分类SUD中具有挑战性的问题。我的 正在进行的NIDA资助的R01使用了从多个基因研究中收集的大样本(n=~12,000) 可卡因、阿片类药物和酒精依赖开发和评估新的统计模型 针对基因发现进行了优化的SUD亚型。K02提案将这项工作扩展到评估治疗 使用可卡因、阿片类药物、 酒精和多种物质依赖。该项目将整合来自诊断行为的数据 变量和基因类型，以及疾病和反复发作的生物/神经生物学特征 治疗结果的衡量标准。本申请的主要职业发展目标是：(1) 了解各种表型方法的可靠性、有效性和作用机制；(2)继续 对成瘾的遗传学进行培训；以及(3)加深对不同治疗方法和 它们的功效。这些领域的坚实基础将增强我实现数据全部潜力的能力 从多个维度收集和汇总，并使用这些数据来设计最具临床实用价值的 分析并生成创新的解决方案，以应对SUD研究中的诊断和预测性挑战。穿过 正式课程、定向阅读、个人辅导以及与 由世界知名研究人员组成的多元化团队，我将接受培训，并为未来的R01项目收集试点数据 通过检查(目标I)：我当前的R01项目中是否派生了临床定义的高度可遗传的亚型 预测不同的治疗反应；(目标二)直接结合治疗的新统计模型 具有行为学、生物学和基因组数据的数据用确认性的多水平识别精细化的亚型 证据；以及(目标III)是否存在按亚型划分的治疗结果的遗传和社会调节因素。这个 这项提议的总体目标是促进我在 开发统计方法对肥皂水进行精细分类。K02奖将为我提供 充分参与所述培训活动所需的受保护时间，这将增加我的知识 和技能，使我能够对SUD的遗传学和治疗做出重要的、新颖的贡献。