Adaptive Automated Scientific Laboratory

自适应自动化科学实验室

• 批准号: EP/M015661/1
• 负责人: Larisa Soldatova
• 金额: $ 46万
• 依托单位: Brunel University London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM015661%2F1
• 关键词: Adaptive; Automated; Scientific; Laboratory

Our proposal integrates the scientific method with 21st century automation technology, with the goal of making scientific discovery more efficient (cheaper, faster, better). A "Robot Scientist" is a physically implemented laboratory automation system that exploits techniques from the field of artificial intelligence to execute cycles of scientific experimentation. Our vision is that within 10 years many scientific discoveries will be made by teams of human and robot scientists, and that such collaborations between human and robot scientists will produce scientific knowledge more efficiently than either could alone. In this way the productivity of science will be increased, leading to societal benefits: better food security, better medicines, etc. The Physics Nobel Laureate Frank Wilczek has predicted that the best scientist in one hundred years time will be a machine. The proposed project aims to take that prediction several steps closer.We will develop the AdaLab (an Adaptive Automated Scientific Laboratory) framework for semi-automated and automated knowledge discovery by teams of human and robot scientists. This framework will integrate and advance a number of ICT methodologies: knowledge representation, ontology engineering, semantic technologies, machine learning, bioinformatics, and automated experimentation (robot scientists). We will evaluate the AdaLab framework on an important real-world application in cell biology with biomedical relevance to cancer and ageing. The core of AdaLab will be generic.The expected project outputs include:- An AdaLab demonstrated to be greater than 20% more efficient at discovering scientific knowledge (within a limited scientific domain) than human scientists alone.- A novel ontology for modelling uncertain knowledge that supports all aspects of the proposed AdaLab framework.- The first ever communication mechanism between human and robot scientists that standardises modes of communication, information exchange protocols, and the content of typical messages. - New machine learning methods for the generation and efficient testing of complex scientific hypotheses that are twice as efficient at selecting experiments as the best current methods.- A significant advance in the state-of-the-art in automating scientific discovery that demonstrates its scalability to problems an order of magnitude more complex than currently possible.- Novel biomedical knowledge about cell biology relevant to cancer and ageing. - A strengthened interdisciplinary research community that crosses the boundaries between multiple ICT disciplines, laboratory automation, and biology.All outputs produced by the project will be made publicly available by the end of the project.

我们的建议将科学方法与21世纪的自动化技术相结合，目标是使科学发现更有效（更便宜、更快、更好）。“机器人科学家”是一种物理实现的实验室自动化系统，它利用人工智能领域的技术来执行科学实验的循环。我们的愿景是，在10年内，许多科学发现将由人类和机器人科学家组成的团队做出，人类和机器人科学家之间的这种合作将比单独一人更有效地产生科学知识。通过这种方式，科学的生产力将得到提高，从而带来社会效益：更好的食品安全、更好的药品等等。诺贝尔物理学奖得主弗兰克·威尔切克（Frank Wilczek）预测，100年后最优秀的科学家将是一台机器。拟议中的项目旨在使这一预测更近一步。我们将开发AdaLab（一个自适应自动化科学实验室）框架，用于人类和机器人科学家团队的半自动和自动化知识发现。该框架将整合和推进许多ICT方法：知识表示、本体工程、语义技术、机器学习、生物信息学和自动化实验（机器人科学家）。我们将评估AdaLab框架在与癌症和衰老相关的生物医学细胞生物学中的重要实际应用。AdaLab的核心将是通用的。预期的项目产出包括：-与人类科学家相比，AdaLab在发现科学知识（在有限的科学领域内）方面的效率要高出20%以上。-为不确定知识建模的新本体，支持拟议AdaLab框架的所有方面。-人类和机器人科学家之间的第一个通信机制，将通信模式、信息交换协议和典型消息的内容标准化。-用于生成和有效测试复杂科学假设的新机器学习方法，在选择实验方面的效率是当前最佳方法的两倍。-在自动化科学发现方面取得的最先进的重大进展，证明其可扩展性比目前可能的更复杂的数量级问题。-与癌症和衰老相关的细胞生物学的新生物医学知识。-加强跨学科研究社区，跨越多个ICT学科、实验室自动化和生物学之间的界限。项目产生的所有产出将在项目结束时向公众提供。

项目成果

Automating Sciences: Philosophical and Social Dimensions

科学自动化：哲学和社会维度

• DOI: 10.1109/mts.2018.2795097
• 发表时间: 2018
• 期刊: IEEE Technology and Society Magazine
• 影响因子: 2.2
• 作者: King R

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

• DOI: 10.1038/s41540-021-00200-x
• 发表时间: 2021-10-20
• 期刊: NPJ systems biology and applications
• 影响因子: 4
• 作者: Wang K;Stevens R;Alachram H;Li Y;Soldatova L;King R;Ananiadou S;Schoene AM;Li M;Christopoulou F;Ambite JL;Matthew J;Garg S;Hermjakob U;Marcu D;Sheng E;Beißbarth T;Wingender E;Galstyan A;Gao X;Chambers B;Pan W;Khomtchouk BB;Evans JA;Rzhetsky A
• 通讯作者: Rzhetsky A

Generic ontology of datatypes

• DOI: 10.1016/j.ins.2015.08.006
• 发表时间: 2016-02-01
• 期刊: INFORMATION SCIENCES
• 影响因子: 8.1
• 作者: Panov, Pance;Soldatova, Larisa N.;Dzeroski, Saso
• 通讯作者: Dzeroski, Saso

SBOL Visual: A Graphical Language for Genetic Designs.

• DOI: 10.1371/journal.pbio.1002310
• 发表时间: 2015-12
• 期刊: PLoS biology
• 影响因子: 9.8
• 作者: Quinn JY;Cox RS 3rd;Adler A;Beal J;Bhatia S;Cai Y;Chen J;Clancy K;Galdzicki M;Hillson NJ;Le Novère N;Maheshwari AJ;McLaughlin JA;Myers CJ;P U;Pocock M;Rodriguez C;Soldatova L;Stan GB;Swainston N;Wipat A;Sauro HM
• 通讯作者: Sauro HM

Testing the reproducibility and robustness of the cancer biology literature by robot.

• DOI: 10.1098/rsif.2021.0821
• 发表时间: 2022-04
• 期刊: Journal of the Royal Society, Interface
• 作者: Roper K;Abdel-Rehim A;Hubbard S;Carpenter M;Rzhetsky A;Soldatova L;King RD
• 通讯作者: King RD