Advancing computational biology and bioinformatics research through open innovation competitions
Advancing computational biology and bioinformatics research through open innovation competitions
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DOI：
10.1371/journal.pone.0222165
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发表时间：
2019-09-27
期刊：
影响因子：
3.7
通讯作者：
Lakhani, Karim R.
中科院分区：
文献类型：
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作者：
Blasco, Andrea;Endres, Michael G.;Lakhani, Karim R.
开放数据科学和算法开发竞赛为快速发现更好的计算策略提供了一个独特的途径。我们突出了计算生物学和生物信息学研究中的三个例子，在这些例子中，使用竞争已经产生了比现有算法显著的性能收益。其中包括抗体聚类、输入基因表达数据和查询连接图(Cmap)的算法。性能收益是使用真实的数据集进行定量评估的，尽管这些数据集经过了清理。然后，就这些竞赛产生的解决办法的效用和在实地实施的前景进行审查。我们介绍了导致这些成功结果的决策过程和竞赛设计考虑因素，作为希望使用竞争和非域人群作为合作者的研究人员进一步研究的模型。
Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.