Delineando las tendencias temáticas en torno a la investigación de métodos computacionales en ciencias de la salud

Autores/as

  • Elsa Carmen Oscuvilca-Tapia Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Fredy Ruperto Bermejo-Sánchez Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Miriam Milagros Noreña-Lucho Universidad Nacional José Faustino Sánchez Carrión image/svg+xml
  • Efraín Ademar Estrada-Choque Universidad Nacional José Faustino Sánchez Carrión image/svg+xml

DOI:

https://doi.org/10.51252/rcsi.v5i1.913

Palabras clave:

métodos computacionales, ciencias de la salud, informática médica, análisis de coocurrencia de términos, bibliometría

Resumen

En este estudio, adoptamos el término método de análisis de coocurrencia para explorar las tendencias temáticas en la literatura sobre métodos computacionales aplicados a las ciencias de la salud. Esbozamos los temas y subtemas clave que caracterizan esta área de investigación. Se utilizó el algoritmo Visualization of Similarities (VOS) para representar las relaciones entre palabras clave en sus grupos temáticos. Se detectaron cinco grupos: métodos computacionales y modelización, química computacional y dinámica molecular, biología computacional y análisis de datos, métodos computacionales y modelos biológicos, y modelización computacional y diseño de fármacos. El estudio indica que la integración de métodos computacionales en las ciencias de la salud es un campo en continua expansión. Las aplicaciones clave incluyen la modelización de procesos biológicos, la simulación de interacciones moleculares y la optimización de tratamientos médicos. Los diversos grupos analizados demuestran que las herramientas computacionales mejoran la exploración de los fenómenos biomédicos y aumentan la precisión de los diagnósticos, la personalización de las terapias y la eficiencia de la investigación farmacéutica.

Citas

Angenent, S., Pichon, E., & Tannenbaum, A. (2006). Mathematical methods in medical image processing. Bulletin of the American mathematical society, 43(3), 365-396. DOI: https://doi.org/10.1090/S0273-0979-06-01104-9

Bardini, R., & Di Carlo, S. (2024). Computational methods for biofabrication in tissue engineering and regenerative medicine-a literature review. Computational and Structural Biotechnology Journal, 23, 601-616. https://doi.org/10.1016/j.csbj.2023.12.035 DOI: https://doi.org/10.1016/j.csbj.2023.12.035

Boccaccio, A., Ballini, A., Pappalettere, C., Tullo, D., Cantore, S., & Desiate, A. (2011). Finite element method (FEM), mechanobiology and biomimetic scaffolds in bone tissue engineering. International journal of biological sciences, 7(1), 112. https://doi.org/10.7150/ijbs.7.112 DOI: https://doi.org/10.7150/ijbs.7.112

Bonafe, C. F. S., Bispo, J. A. C., & de Jesus, M. B. (2018). The polygonal model: A simple representation of biomolecules as a tool for teaching metabolism. Biochemistry and Molecular Biology Education, 46(1), 66-75. https://doi.org/10.1002/bmb.21093 DOI: https://doi.org/10.1002/bmb.21093

Cheng, L., Zhao, H., Wang, P., Zhou, W., Luo, M., Li, T., ... & Jiang, Q. (2019). Computational methods for identifying similar diseases. Molecular Therapy-Nucleic Acids, 18, 590-604. https://doi.org/10.1016/j.omtn.2019.09.019 DOI: https://doi.org/10.1016/j.omtn.2019.09.019

Day, R. S. (2006). Challenges of biological realism and validation in simulation-based medical education. Artificial Intelligence in Medicine, 38(1), 47-66. https://doi.org/10.1016/j.artmed.2006.01.001 DOI: https://doi.org/10.1016/j.artmed.2006.01.001

Faust, O. (2018). Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017. Informatics in Medicine Unlocked, 11, 15-27. https://doi.org/10.1016/j.imu.2018.03.002 DOI: https://doi.org/10.1016/j.imu.2018.03.002

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2020). A review of challenges and opportunities in machine learning for health. AMIA Summits on Translational Science Proceedings, 2020, 191. https://pmc.ncbi.nlm.nih.gov/articles/PMC7233077/

Giorgetti, A., Ruggerone, P., Pantano, S., & Carloni, P. (2012). Advanced computational methods in molecular medicine. Journal of Biomedicine and Biotechnology, 2012. https://doi.org/10.1155/2012/709085 DOI: https://doi.org/10.1155/2012/709085

Jiao, X., Jin, X., Ma, Y., Yang, Y., Li, J., Liang, L., ... & Li, Z. (2021). A comprehensive application: Molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine. Computational Biology and Chemistry, 90, 107402. https://doi.org/10.1016/j.compbiolchem.2020.107402 DOI: https://doi.org/10.1016/j.compbiolchem.2020.107402

Krzywanski, J., Sosnowski, M., Grabowska, K., Zylka, A., Lasek, L., & Kijo-Kleczkowska, A. (2024). Advanced Computational Methods for Modeling, Prediction and Optimization—A Review. Materials, 17(14), 3521. https://doi.org/10.3390/ma17143521 DOI: https://doi.org/10.3390/ma17143521

Lin, H. (2022). Computational methods and resources in biological and medical data. Current Medicinal Chemistry, 29(5), 786-788. https://doi.org/10.2174/092986732905220214141331 DOI: https://doi.org/10.2174/092986732905220214141331

Mathews, J. D., McCaw, C. T., McVernon, J., McBryde, E. S., & McCaw, J. M. (2007). A biological model for influenza transmission: pandemic planning implications of asymptomatic infection and immunity. PLoS One, 2(11), e1220. https://doi.org/10.1371/journal.pone.0001220 DOI: https://doi.org/10.1371/journal.pone.0001220

Mazurek, A. H., Szeleszczuk, Ł., & Pisklak, D. M. (2020). Periodic DFT calculations—review of applications in the pharmaceutical sciences. Pharmaceutics, 12(5), 415. https://doi.org/10.3390/pharmaceutics12050415 DOI: https://doi.org/10.3390/pharmaceutics12050415

Oscuvilca Tapia, E. C., Albitres Infantes, J. J., Cadenas Calderón, P. C., Aguinaga Mendoza, G. M., Paredes Jiménez, H. R., & Andrade Girón, E. C. (2024). Health and medical informatics research: Identifying international collaboration patterns at the country and institution level. Iberoamerican Journal of Science Measurement and Communication, 4(3), 1–16. https://doi.org/10.47909/ijsmc.137 DOI: https://doi.org/10.47909/ijsmc.137

Piana, M. (2009). Computational Methods in Biomedical Imaging. In Encyclopedia of Artificial Intelligence (pp. 372-376). IGI Global. DOI: 10.4018/978-1-59904-849-9.ch057 DOI: https://doi.org/10.4018/978-1-59904-849-9.ch057

Poiate, I. A., Vasconcellos, A. B., Mori, M., & Poiate Jr, E. (2011). 2D and 3D finite element analysis of central incisor generated by computerized tomography. Computer methods and programs in biomedicine, 104(2), 292-299. https://doi.org/10.1016/j.cmpb.2011.03.017 DOI: https://doi.org/10.1016/j.cmpb.2011.03.017

Quesne, M. G., Borowski, T., & de Visser, S. P. (2016). Quantum mechanics/molecular mechanics modeling of enzymatic processes: Caveats and breakthroughs. Chemistry–A European Journal, 22(8), 2562-2581. https://doi.org/10.1002/chem.201503802 DOI: https://doi.org/10.1002/chem.201503802

Sheng, C., & Zhang, W. (2013). Fragment informatics and computational fragment‐based drug design: an overview and update. Medicinal Research Reviews, 33(3), 554-598. https://doi.org/10.1002/med.21255 DOI: https://doi.org/10.1002/med.21255

Shukla, N., Merigó, J. M., Lammers, T., & Miranda, L. (2020). Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017. Computer methods and programs in biomedicine, 183, 105075. https://doi.org/10.1016/j.cmpb.2019.105075 DOI: https://doi.org/10.1016/j.cmpb.2019.105075

Solorzano, G., & Plevris, V. (2022). Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps. Frontiers in Built Environment, 8, 1049616. https://doi.org/10.3389/fbuil.2022.1049616 DOI: https://doi.org/10.3389/fbuil.2022.1049616

Sousa, S. F., Ribeiro, A. J., Neves, R. P., Brás, N. F., Cerqueira, N. M., Fernandes, P. A., & Ramos, M. J. (2017). Application of quantum mechanics/molecular mechanics methods in the study of enzymatic reaction mechanisms. Wiley Interdisciplinary Reviews: Computational Molecular Science, 7(2), e1281. https://doi.org/10.1002/wcms.1281 DOI: https://doi.org/10.1002/wcms.1281

Tang, X. (2019). The role of artificial intelligence in medical imaging research. BJR| Open, 2(1), 20190031. https://doi.org/10.1259/bjro.20190031 DOI: https://doi.org/10.1259/bjro.20190031

Tiwari, G., Shukla, A., Singh, A., & Tiwari, R. (2024). Computer simulation for effective pharmaceutical kinetics and dynamics: a review. Current Computer-Aided Drug Design, 20(4), 325-340. https://doi.org/10.2174/1573409919666230228104901 DOI: https://doi.org/10.2174/1573409919666230228104901

Udupa, J. K. (2005). Display of 3D information in discrete 3D scenes produced by computerized tomography. Proceedings of the IEEE, 71(3), 420-431. https://doi.org/10.1109/PROC.1983.12599 DOI: https://doi.org/10.1109/PROC.1983.12599

Usyk, T. P., & McCulloch, A. D. (2003). Computational methods for soft tissue biomechanics. In Biomechanics of soft tissue in cardiovascular systems (pp. 273-342). Vienna: Springer Vienna. https://doi.org/10.1007/978-3-7091-2736-0_7 DOI: https://doi.org/10.1007/978-3-7091-2736-0_7

Vaishya, R., Gupta, B. M., Singh, Y., Bansal, M., & Vaish, A. (2024). Global research on type 1 diabetes: A bibliometric study during 2001-2022. Iberoamerican Journal of Science Measurement and Communication, 4(2), 1–14. https://doi.org/10.47909/ijsmc.78 DOI: https://doi.org/10.47909/ijsmc.78

Wang, J. (2008). Computational biology of genome expression and regulation—a review of microarray bioinformatics. Journal of Environmental Pathology, Toxicology and Oncology, 27(3). DOI: 10.1615/JEnvironPatholToxicolOncol.v27.i3.10 DOI: https://doi.org/10.1615/JEnvironPatholToxicolOncol.v27.i3.10

Wang, K., Huang, Y., Wang, Y., You, Q., & Wang, L. (2024). Recent advances from computer-aided drug design to artificial intelligence drug design. RSC Medicinal Chemistry, 15(12), 3978-4000. https://doi.org/10.1039/D4MD00522H DOI: https://doi.org/10.1039/D4MD00522H

Wu, Y., Krishnan, S., & Ghoraani, B. (2022). Computational methods for physiological signal processing and data analysis. Computational and Mathematical Methods in Medicine, 2022(1), 9861801. https://doi.org/10.1155/2022/9861801 DOI: https://doi.org/10.1155/2022/9861801

Descargas

Publicado

2025-01-20

Cómo citar

Oscuvilca Tapia, E. C., Bermejo-Sánchez, F. R., Noreña-Lucho, M. M., & Estrada-Choque, E. A. (2025). Delineando las tendencias temáticas en torno a la investigación de métodos computacionales en ciencias de la salud. Revista Científica De Sistemas E Informática, 5(1), e913. https://doi.org/10.51252/rcsi.v5i1.913