AI as an accelerator for defining new problems that transcends boundaries.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Tayo Obafemi-Ajayi, Steven F Jennings, Yu Zhang, Kara Li Liu, Joan Peckham, Jason H Moore
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引用次数: 0

Abstract

Interdisciplinary, transdisciplinary, convergence, and No-Boundary Thinking (NBT) research are methodology and technology-agnostic approaches to problem solving. The focus is on defining problems informed by access to multiple knowledge sources and expert perspectives across different domains, with the goal of accessing all available knowledge sources and perspectives. While access to all available knowledge sources and perspectives could be seen as a difficult to attain objective, with the recent rise of AI we might be closer to approaching this goal. We review several examples of methodologies and technologies that have been used to put these strategies into action, but the primary focus of this paper is on how recent advances in AI now enable a quantum leap forward in defining new problems. By leveraging the capacity of AI to synthesize knowledge from multiple domains, these tools can be used to propose multiple candidate problem definitions. AI is uniquely able to draw upon many more knowledge sources than any individual-or even a very large team-could. Coupled with human intelligence, better problems can be defined to address complex scholarly or societal challenges.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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