生物医学研究的科学发现游戏。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Rhiju Das, Benjamin Keep, Peter Washington, Ingmar H Riedel-Kruse
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引用次数: 17

摘要

在过去的十年里,科学发现游戏(sdg)已经成为生物医学研究的一种可行方法,吸引了成千上万的志愿者参与,并产生了大量的科学出版物。在描述了这种新颖研究方法的起源之后,我们回顾了可持续发展目标在分子建模、序列比对、神经科学、病理学、细胞生物学、基因组学和人类认知方面的科学成果。我们发现在面向问题的游戏(如《Foldit》和《Eterna》)以及面向数据的游戏(如《EyeWire》和《Project Discovery》)中出现了引人注目的结果和技术创新。我们将讨论不同项目中共享的玩家社区的突发性属性,包括社区的多样性和一些志愿者的杰出贡献,如论文撰写。最后,我们强调了与人工智能、生物云实验室、新游戏类型、科学教育和开放科学之间的联系,这些联系可能会推动下一代可持续发展目标的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Scientific Discovery Games for Biomedical Research.

Scientific Discovery Games for Biomedical Research.

Scientific Discovery Games for Biomedical Research.

Over the past decade, scientific discovery games (SDGs) have emerged as a viable approach for biomedical research, engaging hundreds of thousands of volunteer players and resulting in numerous scientific publications. After describing the origins of this novel research approach, we review the scientific output of SDGs across molecular modeling, sequence alignment, neuroscience, pathology, cellular biology, genomics, and human cognition. We find compelling results and technical innovations arising in problem-oriented games such as Foldit and Eterna and in data-oriented games such as EyeWire and Project Discovery. We discuss emergent properties of player communities shared across different projects, including the diversity of communities and the extraordinary contributions of some volunteers, such as paper writing. Finally, we highlight connections to artificial intelligence, biological cloud laboratories, new game genres, science education, and open science that may drive the next generation of SDGs.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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