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大大增强我的研究能力，使我成为一个 在物质使用障碍（SUD）领域的领先定量研究者。具体来说，这将使我能够 提高我在SUD表型，治疗和遗传学领域的知识。泡沫在临床上是 病理学上的异质性及其分类很困难。这个应用程序反映了我的持续 致力于制定有关SUD分类的创新和跨学科研究计划 通过定量分析多维数据。我在计算科学方面的广泛培训和 关于生物医学信息学的事先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，以解决对SUD进行分类的具有挑战性的问题。我的 正在进行的NIDA资助的R01采用了从多种遗传研究的大型（n = 〜12,000）样本 可卡因，阿片类药物和酒精依赖性以开发和评估新型统计模型以产生临床 为基因发现优化的SUD亚型。该K02提案扩展了这项工作以评估治疗 使用可卡因，阿片类药物的治疗研究的数据，在SUD群体的精制亚组中结果 酒精和多种物质依赖性。该项目将整合诊断行为的数据 变量和基因型，以及疾病的生物/神经生物学特征并重复 治疗结果的度量。该应用程序的主要职业发展目标是：（1） 了解各种表型方法的可靠性，有效性和功能机制； （2）继续 培训成瘾的遗传学； （3）以更了解不同的治疗方法和 他们的功效。在这些领域的坚实基础将增强我实现数据的全部潜力的能力 从多个维度收集和汇总，并使用数据设计最有用的 分析并为SUD研究中的诊断和预测挑战生成创新的解决方案。通过 正式的课程工作，定向阅读，个人辅导和密集的多学科合作 多元化的世界知名研究人员团队，我将获得培训并收集未来R01项目的试点数据 通过检查（AIM I）：是否在我当前的R01项目中得出临床定义的高遗传性亚型 预测差异治疗反应； （AIM II）新的统计模型是否直接结合治疗 具有行为，生物学和基因组数据的数据识别具有确认性多级的精制亚型 证据; （AIM III）亚型是否有治疗结果的遗传和社会调节剂。这 该提案的总体目标是进一步进一步我的独立和多学科研究计划 开发用于SUD的精制分类的统计方法。 K02奖将为我提供 充分参与所描述的培训活动所需的保护时间，以增强我的知识 和技能，使我能够为SUD的遗传学和治疗做出重要的新颖贡献。