Boosting the Translational Impact of Scientific Competitions by Ensemble Learning

通过集成学习提升科学竞赛的转化影响

• 批准号: 8864679
• 负责人: Gaurav Pandey
• 金额: $ 44.59万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-07 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8864679
• 关键词: Acute Myelocytic Leukemia; Address; Adopted; Advanced Development; Architecture; Area; Big Data; Biological; Biological Process; Biology; Biomedical Research; Blinded; Characteristics; Clinic; Clinical; Clinical Research; Collaborations; Communication; Communities; Complex; Computer software; Computing Methodologies; Crowding; Data; Data Set; Discipline; Disease; Emerging Technologies; Ensure; Environment; Evaluation; Explosion; Generations; Genomics; Genotype; Goals; Growth; Heterogeneity; High Performance Computing; Image; Incentives; Knowledge; Laboratories; Learning; Life; Machine Learning; Medical; Medicine; Methodology; Methods; Mining; Nature; Outcome; Participant; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Phenotype; Play; Problem Solving; Production; Provider; Public Health; Publications; Research Personnel; Rheumatoid Arthritis; Role; Running; Science; Scientist; Software Design; Solutions; Source; Staging; Synapses; System; Time; Translating; Translations; Validation; Variant; base; clinical application; clinical practice; cohort; computer science; design; innovation; interest; knowledge translation; meetings; method development; novel; open source; predictive modeling; prospective; public health relevance; response; stem; tool

 DESCRIPTION (provided by applicant): "Big data" such as those arising from sequencing, imaging, genomics and other emerging technologies are playing a critical role in modern biology and medicine. The generation of hypotheses about biological processes and disease mechanisms is now increasingly being driven by the production and analysis of large and complex datasets. Advanced computational methods have been developed for the robust analysis of these datasets, and the growth in number and sophistication of these methods has closely tracked the growth in volume and complexity of biomedical data. In such a crowded environment of diverse computational methods and data, it is difficult to judge how generalizable the performance of these methods is from one setting to another. Crowdsourcing-based scientific competitions, or challenges, have now become popular mechanisms for the rigorous, blinded and unbiased evaluation of the performance of these methods and the identification of best-performing methods for biomedical problems. However, despite the benefits of these challenges to the biomedical research enterprise, the impact of their findings has been remarkably limited in laboratory and clinical settings. This is likely due to two important aspects of current challenges: (i) their over-emphasis on identifying the "best" solutions rather than tryig to comprehensively assimilate the knowledge embedded in all the submitted solutions, and (ii) the absence of a stable channel of communication and collaboration between problem and solution providers due to a lack of sufficient incentives to do so. The aim of this project is to boost the translational impact of scientific challenges through a combination of novel machine learning methods, development of novel scalable software and unique collaborations with disease experts to ensure the effective translation of knowledge accrued in challenges to real clinical settings and practice. These novel methods and software are designed to effectively assimilate the knowledge embedded in all the submissions to challenges into "ensemble" solutions. In a first of its kind effort, the ensemble solutions derived from disease-focused challenges under the DREAM project will be brought directly to scientists and clinicians that are experts in these disease areas. Initial effort in this project will focus on active DREAM challenges aiming at the accurate prediction of drug response and clinical outcomes respectively in Rheumatoid Arthritis (RA) and Acute Myeloid Leukemia (AML). Both these diseases are difficult to treat and thus they pose major medical and public health concerns. In collaboration with RA and AML experts, the ensemble solutions learnt in these challenges will be validated in independent patient cohorts and carefully designed clinical studies. This second-level validation is essential to judge the clinical applicability of any method, but is rarely done As the methodology is general, similar efforts will be made for other diseases in later stages of the project. Overall, using a smart combination of crowdsourcing-based challenges and computational methods and software, we aim to demonstrate a unique pathway for studying and treating disease by truly leveraging the "wisdom of the crowds".

 描述（由适用提供）：“大数据”，例如由测序，成像，基因组学和其他新兴技术产生的数据在现代生物学和医学中起着关键作用。现在，关于生物过程和疾病机制的假设的产生越来越多地由大型和复杂数据集的生产和分析驱动。已经开发了用于对这些数据集的强大分析的高级计算方法，这些方法的数量和复杂性的增长已密切跟踪生物医学数据的体积和复杂性的增长。在如此拥挤的潜水计算方法和数据的环境中，很难判断这些方法的性能从一种设置到另一种设置。现在，基于众包的科学竞争或挑战已成为对这些方法的性能进行严格，盲目和无偏见的流行机制，并确定了生物医学问题最佳表现的方法。但是，尽管这些挑战对生物医学研究企业有好处，但其发现的影响在实验室和临床环境中受到了极大的限制。这可能是由于两个重要方面 目前的挑战：（i）他们过分强调确定“最佳”解决方案，而不是尝试全面地吸收所有提交的解决方案中嵌入的知识，以及（ii）由于缺乏足够的激励措施而缺乏问题和解决方案提供者之间稳定的交流和协作渠道。该项目的目的是通过新颖的机器学习方法，新型可扩展软件的开发以及与疾病专家的独特合作的结合来增强科学挑战对科学挑战的翻译影响，以确保对知识的有效翻译在挑战中累积的知识转化为真实的临床环境和实践。这些新颖的方法和软件旨在有效地吸收所有提交中挑战中“集合”解决方案的知识。首先，在梦想项目下以疾病为中心的挑战提出的合奏解决方案将直接带给这些疾病领域专家的科学家和临床医生。该项目的最初努力将集中于积极的梦想挑战，目的是针对类风湿关节炎（RA）和急性髓细胞性白血病（AML）的准确预测药物反应和临床结果。这两种疾病都难以治疗，因此对医疗和公共卫生产生了重大的关注。与RA和AML专家合作，在独立的患者队列和精心设计的临床研究中将验证在这些挑战中学习的集成解决方案。该二级验证对于判断任何方法的临床适用性至关重要，但是很少进行，因为该方法是一般的，在该项目的后期阶段将为其他疾病做出类似的努力。总体而言，使用基于众包的挑战，计算方法和软件的智能组合，我们旨在通过真正利用“人群的智慧”来展示研究和治疗疾病的独特途径。