Critical Assessment of Massive Data Analysis (CAMDA) Conference Series

海量数据分析批判性评估 (CAMDA) 会议系列

• 批准号: 9298332
• 负责人: Wenzhong Xiao
• 金额: $ 0.3万
• 依托单位: INTERNATIONAL SOCIETY/COMP BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-05 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9298332
• 关键词: Critical; Assessment; Massive; Data; Analysis

Recent technological advances have accelerated the development of biomedical research, promising high im- pact findings in both basic research and applied or clinical settings. Exploitation of the measurement data col- lected by the ever more prevalent high throughput assays, in turn, is dependent upon innovation in computa- tional biology and bioinformatics. Crucially, we will require further methodological advances for the detection and identification of novel patterns in the data that are of biomedical relevance. Such algorithmic advances can be hard to evaluate considering a lack of ground truth for truly novel discoveries. Systematic progress, howev- er, relies on regular performance evaluations. To address this open need of the research community, we are running a series of conferences on Critical Assessment of Massive Data Analysis (CAMDA) that are orga- nized as part of the annual ISMB meetings of the International Society for Computational Biology (ISCB), the leading professional organization in the field. CAMDA has become a renowned conference, specializing in ex- amining and driving the cutting edge of complex data analyses in the life sciences. It was originally founded in 2000 (Nature 411, 885. Nature 424, 610) to provide a forum for the critical assessment of different techniques used in large-scale data analysis including, but not limited to high-dimensional gene expression profiling, other -omics, and clinical data (see www.camda.info for general background). It aims to establish the state-of-the-art in analysis methods, identifying progress as well as remaining issues, in order to highlight promising directions for future efforts. Addressing key challenges in the field, CAMDA was one of the first conferences to adopt and optimize the approach of a community-wide contest, with competing experts of the scientific community ana- lysing the same data sets. CAMDA contests focus on technically correct measurements and signal processing on one hand, and the most demanding open ended questions of biomedical inference on the other hand. CAMDA also collaborates with high-profile groups on defining effective contests, challenging the community to deeper analyses of the latest state-of-the-art benchmarks. For instance, several benchmarks generated by the MAQC/SEQC consortia were featured prominently. These consortia were coordinated by the FDA’s NCTR. In fact, CAMDA competitions have included contest data compiled together with the FDA’s NCTR from the very beginning, and regularly since 2012. Researchers worldwide are invited to take the CAMDA challenge, which has already become a prominent fixture (cf. Nature Methods 5, 569), regularly drawing 60–100 specialists from academia and industry. The results and methods of the different contributed analyses are discussed and com- pared at the conference. Selected presentations are published in a special Open Access proceedings issue in collaboration with leading modern publishers, such as F1000 Research, offering fast-track public dissemination and Open Peer Review. Delegates jointly select a winning team and runner-ups. Strikingly, the most impres- sive approaches often come from young scientists in early stages of their careers.

近年来的技术进步加速了生物医学研究的发展，有望实现高质量的生物医学研究。 PACT在基础研究和应用或临床环境中的研究结果。测量数据的开发 由越来越普遍的高通量测定选择，反过来，取决于计算机技术的创新， 生物学和生物信息学。至关重要的是，我们将需要进一步的方法进步， 以及在具有生物医学相关性的数据中识别新模式。这种算法的进步可以 考虑到缺乏真正新颖的发现的基础事实，很难评估。系统的进步，然而- 呃靠定期的绩效考核。为了满足研究界的这一开放需求，我们 举办了一系列关于海量数据分析的关键评估（CAMDA）的会议，这些会议是 作为国际计算生物学学会（ISCB）年度ISMB会议的一部分， 该领域领先的专业组织。CAMDA已成为一个著名的会议，专门从事前， 挖掘和推动生命科学中复杂数据分析的前沿。它最初成立于 2000（Nature 411，885. Nature 424，610），为不同技术的关键评估提供论坛 用于大规模数据分析，包括但不限于高维基因表达谱分析，其他 - 组学和临床数据（参见www.camda.info了解一般背景）。它旨在建立最先进的 在分析方法方面，确定进展情况和剩余问题，以突出有希望的方向 为今后的努力。为了应对这一领域的关键挑战，CAMDA是首批通过和 优化全社区竞赛的方法，与科学界的竞争专家和 裂解相同的数据集。CAMDA竞赛侧重于技术上正确的测量和信号处理 一方面是生物医学推理中最苛刻的开放性问题。 CAMDA还与知名团体合作，定义有效的竞赛，挑战社区， 更深入地分析最新的最先进的基准。例如， MAQC/SEQC联合会的作用十分突出。这些联盟由FDA的NCTR协调。在 事实上，CAMDA竞赛包括与FDA的NCTR一起汇编的竞赛数据， 从2012年开始，定期。世界各地的研究人员被邀请参加CAMDA挑战， 已经成为一个突出的固定装置（cf.自然方法5，569），定期吸引60-100名专家， 学术界和工业界。对不同贡献分析的结果和方法进行了讨论和比较。 在会议上。精选的演讲发表在一个特别的开放获取程序问题上， 与领先的现代出版商合作，如F1000 Research，提供快速的公共传播 开放的同行评审。代表们共同选出一个优胜队和亚军。令人印象深刻的是， 积极的方法往往来自于处于职业生涯早期阶段的年轻科学家。