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.

抽象的 这个独立科学家奖将显着提高我的研究能力，使我能够成为一名 物质使用障碍 (SUD) 领域的领先定量研究人员。具体来说，它将使我能够 增加我在 SUD 表型、治疗和遗传学领域的知识。 SUD 在临床上和 病因学上存在异质性，其分类也很困难。这个应用程序反映了我正在进行的 致力于开发关于 SUD 分类的创新和跨学科研究计划 通过多维数据的定量分析。我在计算科学和 之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可解决 SUD 分类中的挑战性问题。我的 正在进行的 NIDA 资助的 R01 采用了从多项遗传研究中汇总的大量样本（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，以生成临床数据 针对基因发现进行优化的 SUD 亚型。该 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 变量和基因型，以及疾病和重复的生物学/神经生物学特征 治疗结果的衡量标准。此应用程序的主要职业发展目标是：(1) 了解各种表型分析方法的可靠性、有效性和作用机制； (2) 继续 成瘾遗传学培训； (3) 进一步了解不同的治疗方法和 他们的功效。在这些领域打下坚实的基础将增强我充分发挥数据潜力的能力 从多个维度收集和汇总，并利用数据设计出最有临床实用性的方案 分析并生成创新解决方案来应对 SUD 研究中的诊断和预测挑战。通过 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 项目是否衍生出临床定义的高度遗传亚型 预测不同的治疗反应； （目标二）是否有直接结合治疗的新统计模型 包含行为、生物学和基因组数据的数据通过验证性多层次识别精细的亚型 证据; （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 该提案的总体目标是进一步推进我的独立和多学科研究计划 开发 SUD 精细分类的统计方法。 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，这将增强我的知识 和技能使我能够为 SUD 的遗传学和治疗做出重要的、新颖的贡献。