Applicability Of Design Science Research For Biomedical Engineering Research
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Nov 28, 2022
Keywords:
design science research biomedical bioinformatics deep learning
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Abstract
The design-science paradigm aims to push the limits of human and organisational capabilities by generating new and inventive artefacts. The design science research framework includes engineering that applies science through the design process to solve challenges. The design process looks for a solution to meet the environment's needs. On the other hand, a research study results from a collaboration between design and knowledge base to answer a research problem.
The three principal cycles in design science research are the relevance cycle between the environment and the design phase, the design cycle within the design phase, and the rigour cycle between the design phase and the knowledge base. While it is most well-known in the engineering and computer science disciplines, it is also applied in biomedical engineering and bioinformatics. It is relevant in biomedical engineering because of the connection between research outputs and the issue of scientific rigour. Three cases are presented in this article to depict design science study in biomedical and bioinformatics research. For each case discussed, machine learning has a big chance accompanied by unique challenges. In conclusion, the design science paradigm can also be used in biomedical engineering research to ensure that knowledge is relevant and appropriately included in the research process.
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Susanty, M., Fitriah, N., Puspasari, I., Arman, A. A., & Mahayana, D. (2022). Applicability Of Design Science Research For Biomedical Engineering Research. PETIR, 15(2), 219–230. https://doi.org/10.33322/petir.v15i2.1726
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References
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[24] B. Onaral and A. Cohen, “Biomedical Signals,” in Medical Devices and Systems, 3rd ed., J. D. Bronzino, Ed. CRC Press, 2006, pp. 1–22.
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[26] N. Bhattacharyya, “The prevalence of voice problems among adults in the United States,” Laryngoscope, vol. 124, no. 10, pp. 2359–2362, Oct. 2014, doi: 10.1002/LARY.24740.
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[34] A. Gupta and J. Zou, “Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions,” Apr. 2018, Accessed: Dec. 19, 2021. [Online]. Available: https://arxiv.org/abs/1804.01694v1.
[35] Z. Li, Y. Yang, E. Faraggi, J. Zhan, and Y. Zhou, “Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles,” Proteins Struct. Funct. Bioinforma., vol. 82, no. 10, pp. 2565–2573, Oct. 2014, doi: 10.1002/PROT.24620.
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[37] R. Shroff et al., “A structure-based deep learning framework for protein engineering,” bioRxiv, 2019, doi: 10.1101/833905.
[38] N. Anand-Achim et al., “Protein Sequence Design with a Learned Potential,” bioRxiv, p. 2020.01.06.895466, Mar. 2021, doi: 10.1101/2020.01.06.895466.
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[41] M. Karimi, S. Zhu, Y. Cao, and Y. Shen, “De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks,” J. Chem. Inf. Model., vol. 60, no. 12, pp. 5667–5681, Dec. 2020, doi: 10.1021/ACS.JCIM.0C00593/SUPPL_FILE/CI0C00593_SI_001.PDF.
[42] J. Ingraham, V. K. Garg, R. Barzilay, and T. Jaakkola, “Generative models for graph-based protein design,” 2019. Accessed: Mar. 29, 2021. [Online]. Available: https://hdl.handle.net/1721.1/129731.
[43] B. Kuhlman, G. Dantas, G. C. Ireton, G. Varani, B. L. Stoddard, and D. Baker, “Design of a Novel Globular Protein Fold with Atomic-Level Accuracy,” Science (80-. )., vol. 302, no. 5649, pp. 1364–1368, Nov. 2003, doi: 10.1126/SCIENCE.1089427/SUPPL_FILE/1089427S.PDF.
[44] A. E. Donnelly, G. S. Murphy, K. M. Digianantonio, and M. H. Hecht, “A de novo enzyme catalyzes a life-sustaining reaction in Escherichia coli,” Nat. Chem. Biol. 2018 143, vol. 14, no. 3, pp. 253–255, Jan. 2018, doi: 10.1038/nchembio.2550.
[45] B. E. Correia et al., “Proof of principle for epitope-focused vaccine design,” Nat. 2014 5077491, vol. 507, no. 7491, pp. 201–206, Feb. 2014, doi: 10.1038/nature12966.
[46] N. P. King et al., “Computational design of self-assembling protein nanomaterials with atomic level accuracy,” Science (80-. )., vol. 336, no. 6085, pp. 1171–1174, Jun. 2012, doi: 10.1126/SCIENCE.1219364/SUPPL_FILE/DESIGN_MODELS.ZIP.
[47] C. E. Tinberg et al., “Computational design of ligand-binding proteins with high affinity and selectivity,” Nat. 2013 5017466, vol. 501, no. 7466, pp. 212–216, Sep. 2013, doi: 10.1038/nature12443.