Data hazards in synthetic biology.
| Autor: | Zelenka NR; Jean Golding Institute, University of Bristol, Bristol, UK.; BrisEngBio, University of Bristol, Bristol, UK., Di Cara N; School of Psychological Science, University of Bristol, Bristol, UK., Sharma K; School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK., Sarvaharman S; School of Biological Sciences, University of Bristol, Bristol, UK., Ghataora JS; BrisEngBio, University of Bristol, Bristol, UK.; School of Biological Sciences, University of Bristol, Bristol, UK., Parmeggiani F; BrisEngBio, University of Bristol, Bristol, UK.; School of Biochemistry, University of Bristol, Bristol, UK.; School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, UK., Nivala J; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA., Abdallah ZS; School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK., Marucci L; BrisEngBio, University of Bristol, Bristol, UK.; School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK., Gorochowski TE; BrisEngBio, University of Bristol, Bristol, UK.; School of Biological Sciences, University of Bristol, Bristol, UK. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | Synthetic biology (Oxford, England) [Synth Biol (Oxf)] 2024 Jun 21; Vol. 9 (1), pp. ysae010. Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024). |
| DOI: | 10.1093/synbio/ysae010 |
| Abstrakt: | Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology. Competing Interests: None declared. (© The Author(s) 2024. Published by Oxford University Press.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|