Center for Critical Assessment of Genome Interpretation

基因组数据有望彻底改变我们对人类疾病的理解和治疗。多重 在获取数据和实现这些好处和其他好处之间存在障碍。快速积累 基因组数据的多样性远远超出了我们可靠解释基因组变异的能力。人工智能的新发展 智能和机器学习与更强大的计算能力和领域知识相结合，提供 希望在基础研究和临床实践中部署增强的计算工具。使用 这些方法在很大程度上取决于其性能的可靠表征。 基因组解释关键评估中心(C-CAGI)将通过 目的评价人类遗传变异与健康关系的研究现状。CAGI已经有五次 自2010年以来的版本，数百名预测者对社区构成了50个挑战，导致 有关预测方法及其评估的数十篇出版物。我们建议C-CAGI继续 通过以下具体目标推进变式口译领域： 1.开展社区实验，评估口译计算方法的质量 基因组变异数据。C-CAGI将进行社区实验，参与者在其中真诚地 基于基因组数据的疾病相关表型的盲目预测。我们将参与各种不同的 预测者社区，以激励创新。CAGI道德论坛将审查研究，以确保隐私和 分享保持最高标准，并将教育社区。 2.评估当前用于解释基因组变异数据的计算方法的质量； 在互动会议上突出创新和进步。预测将由以下人员评估 独立评估员，他们将得到C-CAGI新的评估方法的支持。结果将是 在CAGI实验会议上展示，具有深入的技术参与，将与 深思熟虑的CAGI？会议，为全面评估外地创造环境，促进 识别当前基因组解释方法所面临的主要瓶颈和问题。 3.广泛传播CAGI实验和分析的结果和结论。C-CAGI 将通过其出版物向更广泛的科学和临床社区进行宣传，并创建一个 已校准的参考集成到最常见的工作流程中，便于采用。CAGI也将 派代表出席国际会议，介绍情况和举办研讨会。 4.有效和负责任地运作。C-CAGI将高效运行，因为它与数百个 参与者的数量。CAGI将建立一个强大的信息基础设施，以安全地促进数据 传播、预测提交和评估。

Genomic data hold the promise of revolutionizing our understanding and treatment of human disease. Multiple barriers stand between the acquisition of the data and realizing these and other benefits. Rapid accumulation of genomic data far exceeds our capacity to reliably interpret genomic variation. New developments in artificial intelligence and machine learning, combined with increased computing power and domain knowledge, provide hope for the deployment of enhanced computational tools in both basic research and clinical practice. Use of these methods critically depends upon reliable characterization of their performance. The Center for Critical Assessment of Genome Interpretation (C-CAGI) will address these needs, through objective evaluation of the state of the art in relating human genetic variation and health. CAGI has had five editions since 2010 with 50 challenges posed to the community taken on by hundreds of predictors, leading to scores of publications about prediction methods and their assessment. We propose for C-CAGI to continue to advance the field of variant interpretation through the following Specific Aims: 1. Develop community experiments to evaluate the quality of computational methods for interpreting genomic variation data. C-CAGI will conduct community experiments in which participants make bona fide blinded predictions of disease related phenotypes on the basis of genomic data. We will engage a diverse predictor community to spur innovation. The CAGI Ethics Forum will vet studies to ensure that privacy and sharing maintain the highest standards and will educate the community. 2. Assess the quality of current computational methods for interpreting genomic variation data; highlight innovations and progress at interactive conferences. Predictions will be evaluated by independent assessors, who will be supported by new assessment approaches from C-CAGI. Results will be presented at CAGI experiment conferences with deep technical engagement, which will be interleaved with reflective CAGIâ meetings that create an environment for a comprehensive evaluation of the field, facilitating identification of major bottlenecks and problems faced by the current genome interpretation approaches. 3. Broadly disseminate the results and conclusions from the CAGI experiments and analysis. C-CAGI will outreach to the broader scientific and clinical community through its publications, and the creation of a calibrated reference integrated into the most common workflows for ready adoption. CAGI will also be represented at international meetings with presentations and workshops. 4. Operate effectively and responsively. C-CAGI will operate efficiently as it closely interacts with hundreds of participants. CAGI will build upon a robust information infrastructure that securely facilitates data dissemination, prediction submission, and assessment.