Implementation of Grid Computing in Genomic Data Processing in Biomedical Informatics

Rahmawati Rahmawati (1), Ammar Al-Momani (2), Sarah Williams (3)
(1) Universitas Almarisah Madani Makassar, Indonesia,
(2) University of Jordan, Jordan,
(3) University of Toronto, Canada

Abstract

The exponential growth of genomic data in biomedical informatics has necessitated efficient computational methods to process and analyze vast datasets. Traditional computational systems often fall short in handling the scale and complexity of genomic data. This study investigates the implementation of grid computing as a scalable and cost-effective solution for genomic data processing in biomedical informatics. The research aims to evaluate the feasibility and performance of grid computing in enhancing data throughput, reducing computational latency, and improving resource utilization in genomic data workflows. The study adopts a methodological approach that integrates grid computing frameworks, such as Globus Toolkit and Apache Hadoop, into genomic data processing pipelines. Simulated genomic datasets and real-world case studies were employed to benchmark the grid computing system against conventional computational environments. The results demonstrate significant improvements in processing speed, with an average reduction of 40% in computational time, and a 25% increase in resource efficiency. Additionally, the system showcased robust scalability, handling up to 10 times larger datasets without compromising accuracy or reliability. In conclusion, the findings underscore the potential of grid computing to revolutionize genomic data processing, making it a pivotal technology in biomedical informatics. This study highlights the importance of adopting distributed computing paradigms to address the challenges posed by modern bioinformatics demands.

Full text article

Generated from XML file

References

Agca, Y., Amos-Landgraf, J., Araiza, R., Brennan, J., Carlson, C., Ciavatta, D., Clary, D., Franklin, C., Korf, I., Lutz, C., Magnuson, T., de Villena, F. P.-M., Mirochnitchenko, O., Patel, S., Port, D., Reinholdt, L., & Lloyd, K. C. K. (2024). The mutant mouse resource and research center (MMRRC) consortium: The US-based public mouse repository system. Mammalian Genome, 35(4), 524–536. Scopus. https://doi.org/10.1007/s00335-024-10070-3

Ahmad, S. S., Khan, S., & Kamal, M. A. (2019). What is blockchain technology and its significance in the current healthcare system? A brief insight. Current Pharmaceutical Design, 25(12), 1402–1408. Scopus. https://doi.org/10.2174/1381612825666190620150302

Ahn, H., Lee, M., Seong, S., Lee, M., Na, G.-J., Chun, I.-G., Kim, Y., & Hong, C.-H. (2023). BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference. Electronics (Switzerland), 12(21). Scopus. https://doi.org/10.3390/electronics12214544

Ammon, D., Kurscheidt, M., Buckow, K., Kirsten, T., Löbe, M., Meineke, F., Prasser, F., Saß, J., Sax, U., Stäubert, S., Thun, S., Wettstein, R., Wiedekopf, J. P., Wodke, J. A. H., Boeker, M., & Ganslandt, T. (2024). Interoperability Working Group: Core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 67(6), 656–667. Scopus. https://doi.org/10.1007/s00103-024-03888-4

Bachelot, G., Dhombres, F., Sermondade, N., Hamid, R. H., Berthaut, I., Frydman, V., Prades, M., Kolanska, K., Selleret, L., Mathieu-D’Argent, E., Rivet-Danon, D., Levy, R., Lamazière, A., & Dupont, C. (2023). A Machine Learning Approach for the Prediction of Testicular Sperm Extraction in Nonobstructive Azoospermia: Algorithm Development and Validation Study. Journal of Medical Internet Research, 25. Scopus. https://doi.org/10.2196/44047

Back, C. O., Manataki, A., & Harrison, E. (2020). Mining patient flow patterns in a surgical ward. Dalam Cabitza F., Fred A., & Gamboa H. (Ed.), HEALTHINF - Int. Conf. Health Informatics, Proc.; Part Int. Jt. Conf. Biomed. Eng. Syst. Technol., BIOSTEC (hlm. 273–283). SciTePress; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083715322&partnerID=40&md5=b84fed7726ab1b11eea78a1bd7b35193

Butdisuwan, S., M. Annamma, L., Subaveerapandiyan, A., George, B. T., & Kataria, S. (2024). Visualising Medical Research: Exploring the Influence of Infographics on Professional Dissemination. Scientific World Journal, 2024. Scopus. https://doi.org/10.1155/2024/5422121

Channi, H. K., Kumar, P., & Singh, P. (2024). Computational and Blockchain Methods in Distributed Biomedical and Health Informatics: Applications, Architecture, Applications, and Challenges. Dalam Computational Intelligence and Blockchain in Biomedical and Health Informatics (hlm. 134–154). CRC Press; Scopus. https://doi.org/10.1201/9781003459347-10

Ciszek, R., Ndode-Ekane, X. E., Gomez, C. S., Casillas-Espinosa, P. M., Ali, I., Smith, G., Puhakka, N., Lapinlampi, N., Andrade, P., Kamnaksh, A., Immonen, R., Paananen, T., Hudson, M. R., Brady, R. D., Shultz, S. R., O’Brien, T. J., Staba, R. J., Tohka, J., & Pitkänen, A. (2019). Informatics tools to assess the success of procedural harmonization in preclinical multicenter biomarker discovery study on post-traumatic epileptogenesis. Epilepsy Research, 150, 17–26. Scopus. https://doi.org/10.1016/j.eplepsyres.2018.12.010

Daniel, C., & Kalra, D. (2019). Clinical Research Informatics: Contributions from 2018. Yearbook of Medical Informatics, 28(1), 203–205. Scopus. https://doi.org/10.1055/s-0039-1677921

De Vila, M. H., Attar, R., Pereanez, M., & Frangi, A. F. (2019). MULTI-X, a State-of-the-Art Cloud-Based Ecosystem for Biomedical Research. Dalam Schmidt H., Griol D., Wang H., Baumbach J., Zheng H., Callejas Z., Hu X., Dickerson J., & Zhang L. (Ed.), Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM (hlm. 1726–1733). Institute of Electrical and Electronics Engineers Inc.; Scopus. https://doi.org/10.1109/BIBM.2018.8621317

Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C.-W. (2022). Secure Collaborative Augmented Reality Framework for Biomedical Informatics. IEEE Journal of Biomedical and Health Informatics, 26(6), 2417–2424. Scopus. https://doi.org/10.1109/JBHI.2021.3139575

Elangovan, D., Long, C. S., Bakrin, F. S., Tan, C. S., Goh, K. W., Yeoh, S. F., Loy, M. J., Hussain, Z., Lee, K. S., Idris, A. C., & Ming, L. C. (2022). The Use of Blockchain Technology in the Health Care Sector: Systematic Review. JMIR Medical Informatics, 10(1). Scopus. https://doi.org/10.2196/17278

Elghriani, A. M., Maatuk, A. M., Elberkawi, E. K., & El-Turki, T. (2021). Evaluation of the Knowledge of Medical and Health Informatics Students for Bioinformatics and Biomedical Research. ACM Int. Conf. Proc. Ser. ACM International Conference Proceeding Series. Scopus. https://doi.org/10.1145/3492547.3492588

Erryani, A., Rahmah, A., Asmaria, T., Lestari, F. P., & Kartika, I. (2021). Microstructure and Corrosion Behavior of Bioabsorbable Polymer Polylactic Acid-Polycaprolactone Reinforced by Magnesium-Zinc Alloy for Biomedical Application. Dalam Triwiyanto T., Nugroho H.A., Rizal A., & Caesarendra W. (Ed.), Lect. Notes Electr. Eng.: Vol. 746 LNEE (hlm. 377–386). Springer Science and Business Media Deutschland GmbH; Scopus. https://doi.org/10.1007/978-981-33-6926-9_32

Facco Rodrigues, V., da Rosa Righi, R., André da Costa, C., Eskofier, B., & Maier, A. (2019). On Providing Multi-Level Quality of Service for Operating Rooms of the Future. Sensors (Basel, Switzerland), 19(10). Scopus. https://doi.org/10.3390/s19102303

Feldmeth, G., Naureckas, E. T., Solway, J., & Lindau, S. T. (2019). Embedding research recruitment in a community resource e-prescribing system: Lessons from an implementation study on Chicago’s South Side. Journal of the American Medical Informatics Association, 26(8–9), 840–846. Scopus. https://doi.org/10.1093/jamia/ocz059

Grissette, H., & Nfaoui, E. H. (2020). Enhancing convolution-based sentiment extractor via dubbed N-gram embedding-related drug vocabulary. Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1). Scopus. https://doi.org/10.1007/s13721-020-00248-5

Hawari K.B.G., Singh P.K., Luhach A.K., Singh D., Hsiung P.-A., & Lingras P. (Ed.). (2019). 2nd International Conference on Advanced Informatics for Computing Research, ICAICR 2018. Communications in Computer and Information Science, 955. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059026729&partnerID=40&md5=594d02198c9638d641ab0333672ec46b

He, H., Zhang, M., Hao, M., Du, W., & Xia, H. (2024). Synthesis of Different Aspect-Ratios of Fixed Width Gold Nanorods. Wuli Huaxue Xuebao/ Acta Physico - Chimica Sinica, 40(5). Scopus. https://doi.org/10.3866/PKU.WHXB202304043

Hund, H., Wettstein, R., Heidt, C. M., & Fegeler, C. (2021). Executing distributed healthcare and research processes—The highmed data sharing framework. Dalam 34042885, Ger. Med. Data Sci.: Bringing Data to Life: Proc. Of the Jt. Annual Meet. Of the Ger. Association of Med. Inform., Biometry and Epidemiol. (Gmds e.V.) and the Cent. Eur. Netw. - Int. Biom. Soc. (CEN-IBS) 2020 in Berl., Ger. (Vol. 278, hlm. 126–133). IOS Press; Scopus. https://doi.org/10.3233/SHTI210060

Jackson, G., & Hu, J. (2019). Artificial Intelligence in Health in 2018: New Opportunities, Challenges, and Practical Implications. Yearbook of Medical Informatics, 28(1), 52–54. Scopus. https://doi.org/10.1055/s-0039-1677925

Justinia, T. (2019). Blockchain technologies: Opportunities for solving real-world problems in healthcare and biomedical sciences. Acta Informatica Medica, 27(4), 284–291. Scopus. https://doi.org/10.5455/aim.2019.27.284-291

Kaiser M.S., Bandyopadhyay A., Ray K., Singh R., & Nagar V. (Ed.). (2022). 1st International Conference on Trends in Electronics and Health Informatics, TEHI 2021. Lecture Notes in Networks and Systems, 376. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127662498&partnerID=40&md5=978c07f116157c88d01ebe2d7f893602

Kalra, D. (2019). Raising the Impact of Real World Evidence. Studies in Health Technology and Informatics, 258, 1. Scopus.

Lella, L., & Piersantelli, S. (2020). A novel blockchain based platform to support chronic care model information management. Dalam Cabitza F., Fred A., & Gamboa H. (Ed.), HEALTHINF - Int. Conf. Health Informatics, Proc.; Part Int. Jt. Conf. Biomed. Eng. Syst. Technol., BIOSTEC (hlm. 303–309). SciTePress; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083720623&partnerID=40&md5=95d58853bdb88f5180e6db97a3b8a5e1

Lungeanu, D., Petrica, A., Lupusoru, R., Marza, A. M., Mederle, O. A., & Timar, B. (2022). Beyond the Digital Competencies of Medical Students: Concerns over Integrating Data Science Basics into the Medical Curriculum. International Journal of Environmental Research and Public Health, 19(23). Scopus. https://doi.org/10.3390/ijerph192315958

Lyman, D. F., Bell, A., Black, A., Dingerdissen, H., Cauley, E., Gogate, N., Liu, D., Joseph, A., Kahsay, R., Crichton, D. J., Mehta, A., & Mazumder, R. (2022). Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model. Glycobiology, 32(10), 855–870. Scopus. https://doi.org/10.1093/glycob/cwac046

Ma, X., Zhang, L., Wang, J., & Luo, Y. (2019). Knowledge domain and emerging trends on echinococcosis research: A scientometric analysis. International Journal of Environmental Research and Public Health, 16(5). Scopus. https://doi.org/10.3390/ijerph16050842

Maglogiannis I., Iliadis L., Macintyre J., & Cortez P. (Ed.). (2022). 11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022. IFIP Advances in Information and Communication Technology, 652 IFIP. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133266414&partnerID=40&md5=909390d2625f8a5a25f24736706d0b20

Nichols J., Maccabe A.B., Parete-Koon S., Verastegui B., Hernandez O., & Ahearn T. (Ed.). (2021). 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020. Communications in Computer and Information Science, 1315 CCIS. Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107301857&partnerID=40&md5=8b5041dd6cd83b68ffe844156f35f282

Nunes, P., Jesus, R., Lebre, R., & Costa, C. (2020). Data and sessions management in a telepathology platform. Dalam Cabitza F., Fred A., & Gamboa H. (Ed.), HEALTHINF - Int. Conf. Health Informatics, Proc.; Part Int. Jt. Conf. Biomed. Eng. Syst. Technol., BIOSTEC (hlm. 455–462). SciTePress; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083695241&partnerID=40&md5=100028d412fb229fc8dde31521f3161c

Prediger, L., Jälkö, J., Honkela, A., & Kaski, S. (2024). Collaborative learning from distributed data with differentially private synthetic data. BMC Medical Informatics and Decision Making, 24(1). Scopus. https://doi.org/10.1186/s12911-024-02563-7

Scheel, H., Dathe, H., Franke, T., Scharfe, T., & Rottmann, T. (2019). A privacy preserving approach to feasibility analyses on distributed data sources in biomedical research. Dalam Rohrig R., Binder H., Prokosch H.-U., Sax U., Schmidtmann I., Stolpe S., & Zapf A. (Ed.), Stud. Health Technol. Informatics (Vol. 267, hlm. 254–261). IOS Press; Scopus. https://doi.org/10.3233/SHTI190835

Shin, S. J., You, S. C., Roh, J., Park, Y. R., & Park, R. W. (2019). Genomic common data model for biomedical data in clinical practice. Dalam Seroussi B., Ohno-Machado L., Ohno-Machado L., & Seroussi B. (Ed.), Stud. Health Technol. Informatics (Vol. 264, hlm. 1843–1844). IOS Press; Scopus. https://doi.org/10.3233/SHTI190676

Teeple, E., Kuhlman, C., Werner, B., Paffenroth, R., & Rundensteiner, E. (2020). Air quality and cause-specific mortality in the United States: Association analysis by regression and CCA for 1980-2014. Dalam Cabitza F., Fred A., & Gamboa H. (Ed.), HEALTHINF - Int. Conf. Health Informatics, Proc.; Part Int. Jt. Conf. Biomed. Eng. Syst. Technol., BIOSTEC (hlm. 228–236). SciTePress; Scopus. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083691472&partnerID=40&md5=bf6b98c778f61f0f67042c0239e10030

Uddin, M. (2019). Design of biomedical informatics framework for personalized medicine in healthcare organizations. Dalam Seroussi B., Ohno-Machado L., Ohno-Machado L., & Seroussi B. (Ed.), Stud. Health Technol. Informatics (Vol. 264, hlm. 1612–1613). IOS Press; Scopus. https://doi.org/10.3233/SHTI190560

Ultsch, A., & Lötsch, J. (2022). Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans). BMC Bioinformatics, 23(1). Scopus. https://doi.org/10.1186/s12859-022-04769-w

Winkler, S., Huber, M., & Kluge, T. (2019). Achieving an interoperable data format for neurophysiology with DICOM waveforms. Dalam Hayn D., Eggerth A., & Schreier G. (Ed.), Stud. Health Technol. Informatics (Vol. 260, hlm. 97–104). IOS Press; Scopus. https://doi.org/10.3233/978-1-61499-971-3-97

Zilske, C., Kurscheidt, M., Schweizer, S. T., Hund, H., Mödinger, S., & Fegeler, C. (2023). Monitoring Distributed Business Processes in Biomedical Research. Dalam Hagglund M., Blusi M., Bonacina S., Nilsson L., Madsen I.C., Pelayo S., Moen A., Benis A., Lindskold L., & Gallos P. (Ed.), Stud. Health Technol. Informatics (Vol. 302, hlm. 252–256). IOS Press BV; Scopus. https://doi.org/10.3233/SHTI230113

Authors

Rahmawati Rahmawati
rahmawati@univeral.ac.id (Primary Contact)
Ammar Al-Momani
Sarah Williams
Rahmawati, R., Al-Momani, A., & Williams, S. . (2024). Implementation of Grid Computing in Genomic Data Processing in Biomedical Informatics. Journal of Computer Science Advancements, 2(6), 447–461. https://doi.org/10.70177/jsca.v2i6.1618

Article Details

Most read articles by the same author(s)

Optimization of Grid Computing for Big Data Processing in Biomedical Research

Devi Rahmah Sope, Wolnough Cale, M. Anwar Aini, Nur Fajrin Maulana Yusuf, Masli Nurcahya Zoraida
Abstract View : 31
Download :17