THE ROLE OF BIOMEDICAL INFORMATICS IN THE STUDY OF BRONCHOPULMONARY DYSPLASIA

DOI: 10.34689/SH.2024.26.6.019

Introduction. The review examines the potential for using biomedical informatics to advance bronchopulmonary dysplasia (BPD) research and address challenges and opportunities for personalized medicine and predictive analytics. This review aims to analyze the possibilities of using biomedical informatics to study bronchopulmonary dysplasia. Methods: This review was conducted using electronic search engines such as PubMed and Google Scholar to identify publications exploring the application of biomedical informatics in the study of bronchopulmonary dysplasia. The search depth was 7 years, from 2018 to 2024. Inclusion criteria were full-text articles in English. Exclusion criteria were articles in other languages and closed access. Results: Biomedical informatics (BMI) is a powerful set of tools designed to manage and analyze large and diverse biomedical data. In addition, biomedical informatics can greatly contribute to BPD research, embracing genomics and personalized medicine to identify potential biomarkers and develop personalized treatment strategies. Predictive analytics is becoming a key aspect of BMI, providing early diagnosis and risk assessment for timely intervention. Moreover, the ethical and legal aspects associated with implementing BMI in BPD research are addressed. Conclusion: Biomedical informatics has the potential to provide greater insight into the pathogenesis of BPD, facilitating personalized approaches to neonatal care. It will play an important role in opening new frontiers in BPD prognosis, prevention, and treatment.

Тарабаева Анель Саидовна - к.м.н., профессор кафедры общей иммунологии им. А.А. Шортанбаева, НАО «Казахский национальный медицинский университет им. С.Д. Асфендиярова», г. Алматы, Республика Казахстан. Почтовый адрес: Республика Казахстан, 050000, г. Алматы, ул. Богенбай батыра, 153; E-mail: tarabaeva.a@kaznmu.kz Телефон: 8 701 204 20 95 Автор-корреспондент: Абильбаева Арайлым Асылхановна – PhD, ассоциированный профессор без звания кафедры общей иммунологии им. А.А. Шортанбаева, НАО «Казахский национальный медицинский университет им. С.Д. Асфендиярова», г. Алматы, Республика Казахстан. Почтовый адрес: Республика Казахстан, 050000, г. Алматы, ул. Богенбай батыра, 153; E-mail: abilbaeva.a@kaznmu.kz Телефон: + 7 702 214 89 65, 8 708 347 62 77

1. Álvarez-Fuente M., Moreno L., Mitchell J.A., Reiss I.K. et al. Preventing bronchopulmonary dysplasia: new tools for an old challenge. Pediatr Res, 2019.Vol.85.I.4.P.432-441. doi: 10.1038/s41390-018-0228-0. 2. Athanasopoulou K., Daneva G.N., Adamopoulos P.G., Scorilas A. Artificial intelligence: The milestone in modern biomedical research. Bio Med Informatics, 2022. Vol. 2. P.727–744. https://doi.org/10.3390/biomedinformatics2040049. 3. Auslander N., Gussow A.B., Koonin E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int J Mol Sci, 2021.Vol.22.I.6.P.2903. https://doi.org/10.3390/ijms22062903. 4. Chang P.D., Wong T.T., Rasiej M.J. Deep learning for detection of complete anterior cruciate ligament tear// J. Digit. Imaging, 2019. Vol.32. I.6. P.980–986. https://doi.org/10.1007/s10278-019-00193-4. 5. Chen C., Wang J., Pan D., Wang X., Xu Y., Yan J., Wang L., Yang X., Yang M., Liu G.P. Applications of multi-omics analysis in human diseases // MedComm, 2023. Vol.4.I.4.P.e315. doi: 10.1002/mco2.315. 6. Clough E., Barrett T., Wilhite S.E., Ledoux .P, Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., et al. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res, 2024. Vol.52. I.1. P.138-144. doi: 10.1093/nar/gkad965. 7. Conesa A., Beck S. Making multi-omics data accessible to researchers. Sci Data, 2019. Vol.6. P.251. https://doi.org/10.1038/s41597-019-0258-4. 8. Dai D., Chen H., Dong X., Chen J., Mei M., Lu Y., Yang L., Wu B., Cao Y., Wang J., Zhou W., Qian L. Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information. Front Genet, 2021.Vol.12.P.689071. doi: 10.3389/fgene.2021.689071. 9. Dankhara N., Holla I., Ramarao S., Kalikkot Thekkeveedu R. Bronchopulmonary Dysplasia: Pathogenesis and Pathophysiology. Journal of Clinical Medicine, 2023. Vol.12.I13.P.4207. https://doi.org/10.3390/jcm12134207. 10. de Brevern A.G. Bio Med Informatics, the Link between Biomedical Informatics, Biology and Computational Medicine. Bio Med. Informatics, 2024. Vol.4. P.1-7. https://doi.org/10.3390/biomedinformatics4010001. 11. Dhrithi D., Karishma C., Tejasvene R., Matteo P., Sagiv S., Malak S. A., et al. The evolution of computational research in a data-centric world. Cell, 2024. Vol.187. I.17. P.4449-4457. https://doi.org/10.1016/j.cell.2024.07.045. 12. Dini G., Ceccarelli S., Celi F. Strategies for the prevention of bronchopulmonary dysplasia. Front Pediatr. 2024. Vol.12. P.e1439265. doi: 10.3389/fped.2024.1439265. 13. Fennelly O., Grogan L., Reed A., Hardiker N.R. Use of standardized terminologies in clinical practice: A scoping review. Int J Med Inform, 2021. Vol.149. P.104431. doi: 10.1016/j.ijmedinf.2021.104431. 14. Ferizi U., Besser H., Hysi P., Jacobs J., Rajapakse C.S., Chen C., Saha P.K., Honig S., Chang G. Artificial intelligence applied to osteoporosis: A performance comparison of machine learning algorithms in predicting fragility fractures from MRI data. J. Magn. Reson. Imaging, 2019. Vol.49. I.4. P.1029–1038. https://doi.org/10.1002/jmri.26280. 15. Gao Y., Liu D., Guo Y., Cao M. Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model. Front Pediatr, 2023. Vol.11. P.1117142. doi: 10.3389/fped.2023.1117142. 16. Gilfillan M.A., Mejia M.J., Bhandari V. Prevalence, Prevention and Management of Bronchopulmonary Dysplasia. Research and Reports in Neonatology, 2024. Vol. 14. P.1-33. https://doi.org/10.2147/RRN.S405985. 17. Gillespie M., Jassal B., Stephan R., Milacic M., Rothfels K., Senff-Ribeiro A., et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res, 2022. Vol.50. I.1. P.687-692. doi: 10.1093/nar/gkab1028. 18. Hersh W.R. Chapter 1: Introduction to Biomedical and Health Informatics. In Health Informatics: Practical Guide, 8th ed., Lulu Press, Inc.: Morrisville, NC, USA, 2022. P.1-18. 19. Hubner U.H., Wilson G.M., Morawski T.S., Ball M.J. (Eds.). Nursing Informatics: A Health Informatics, Interprofessional and Global Perspective, 5th ed.; Springer: Cham, Switzerland, 2022. P.15-38. 20. Hunt G.P., Grassi L., Henkin R., Smeraldi F., Spargo T.P., Kabiljo R., Koks S., Ibrahim Z., Dobson R.J.B., Al-Chalabi A., Barnes M.R., Iacoangeli A. GEOexplorer: a webserver for gene expression analysis and visualization. Nucleic Acids Res, 2022. Vol.50. I.1. P.367-374. doi: 10.1093/nar/gkac364. 21. Jensen E.A., Dysart K., Gantz M.G., McDonald S., Bamat N.A. et al. The Diagnosis of Bronchopulmonary Dysplasia in Very Preterm Infants. An Evidence-based Approach. Am J Respir Crit Care Med, 2019. Vol.200. I.6. P.751-759. doi: 10.1164/rccm.201812-2348OC. 22. Johnson T.R., Bernstam E.V. Why is biomedical informatics hard? A fundamental framework. J Biomed Inform. 2023. Vol.140.P.e104327. doi: 10.1016/j.jbi.2023.104327. 23. Kalyan G.U., Pranathi Reddy D., Chandrakanth G., Pooja B., Anitha V., Vivek D. "Gene Association Disease Prediction by GEO2R Tool", 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT), 2023. P.1-6, doi: 10.1109/EASCT59475.2023.10392802. 24. Kanehisa M., Furumichi M., Sato Y., Kawashima M., Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res, 2023. Vol.51. I.1. P.587-592. doi: 10.1093/nar/gkac963. 25. Kanehisa M., Sato Y., Kawashima M. KEGG mapping tools for uncovering hidden features in biological data. Protein Sci, 2022. Vol.31. I.1. P.47-53. doi: 10.1002/pro.4172. 26. Karimi D., Peters J.M., Ouaalam A., Prabhu S.P., Sahin M., Krueger D.A., et al. Learning to detect brain lesions from noisy annotations. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020. P. 1910–1914. 27. Katsoulakis E., Wang Q., Wu H. et al. Digital twins for health: a scoping review. NPJ Digit. Med., 2024. Vol.7. P.77. https://doi.org/10.1038/s41746-024-01073-0 28. Kumuthini J., Chimenti M., Nahnsen S., et al. Ten simple rules for providing effective bioinformatics research support. PLoS Comput Biol, 2020. Vol.16. P.e1007531. doi.10.1371/journal.pcbi.1007531. 29. Lao Y., Jia B., Yan P., Pan M., Hui X., Li J., Luo W., Li X., Han J., Yan P., Yao L. Diagnostic accuracy of machine-learning-assisted detection for anterior cruciate ligament injury based on magnetic resonance imaging: Protocol for a systematic review and meta-analysis. Medicine (Baltimore), 2019. Vol.98. I.50. P.e18324. doi: 10.1097/MD.0000000000018324. 30. Lei J., Sun T., Jiang Y., Wu P., Fu J., Zhang T., McGrath E. Risk Identification of Bronchopulmonary Dysplasia in Premature Infants Based on Machine Learning. Front Pediatr, 2021. Vol.9. P.e719352. doi: 10.3389/fped.2021.719352. 31. Liu W., Su Y., Li S., Chen H., Liu Y., Li X., Shen W., Zhong X., Wu F., Meng Q., Jiang X. Weighted gene coexpression network reveals downregulation of genes in bronchopulmonary dysplasia. Pediatr Pulmonol, 2021. Vol.56. I.2. P.392-399. doi: 10.1002/ppul.25141. 32. Ma X.-K., Yu Y., Huang T., et al. Bioinformatics software development: Principles and future directions// The Innovation Life. 2024. Vol.2.I.3. P.e100083. https://doi.org/10.59717/j.xinn-life.2024.100083. 33. Manzoni C., Kia D.A., Vandrovcova J., Hardy J., Wood N.W., Lewis P.A., Ferrari R. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform, 2018. Vol.19. I.2. P.286-302. doi: 10.1093/bib/bbw114. 34. Mohr A.E., Ortega-Santos C.P., Whisner C.M., Klein-Seetharaman J., Jasbi P. Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare. Biomedicines, 2024. Vol.12. P.1496. https://doi.org/10.3390/biomedicines12071496. 35. Morag I., Barkai E., Wazana Y., Elizur A., Levkovitz Stern O., Staretz-Chacham O., Pinchevski-Kadir S., Ofek Shlomai N. Predictors of Developmental and Respiratory Outcomes Among Preterm Infants With Bronchopulmonary Dysplasia. Front Pediatr, 2021. Vol.9. P.e780518. doi: 10.3389/fped.2021.780518. 36. Moreira A., Tovar M., Smith A.M., et al. Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia. Am J Physiol Lung Cell Mol Physiol, 2023. Vol.324. P.76-87. doi:10.1152/ajplung.00250.2022. 37. Moreira A.G., Arora T., Arya S., Winter C., Valadie C.T., Kwinta P. Leveraging transcriptomics to develop bronchopulmonary dysplasia endotypes: a concept paper. Respir Res, 2023. Vol.24. I.1. P.284. doi: 10.1186/s12931-023-02596-y. 38. Oliva A., Grassi S., Vetrugno G., et al. Management of Medico-Legal Risks in Digital Health Era: A Scoping Review. Front Med (Lausanne), 2021. Vol.8. P.821756. doi:10.3389/fmed.2021.821756. 39. Pais R.J. Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups. BioTech (Basel), 2022. Vol.11. I.3. P.35.doi: 10.3390/biotech11030035. PMID: 35997343; PMCID: PMC9397027. 40. Pomaznoy M., Ha B., Peters B. GOnet: a tool for interactive Gene Ontology analysis. BMC Bioinformatics, 2018. Vol.19. P.470. https://doi.org/10.1186/s12859-018-2533-3 41. Ranganathan S., Nakai K., Schönbach C. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics/ 1st ed. Elsevier, 2019. P.3284. 42. Richardson A., Ormond K.E. Ethical considerations in prenatal testing: Genomic testing and medical uncertainty. Semin Fetal Neonatal Med, 2018.Vol.23.P.1-6. doi: 10.1016/j.siny.2017.10.001. 43. Richesson R.L., Andrews J.E., Hollis K.F. (Eds.) Clinical Research Informatics, 3rd ed.; Springer: Cham, Switzerland, 2023.P.231-252. 44. Robi Y.G., Sitote T.M. Neonatal Disease Prediction Using Machine Learning Techniques. J Healthc Eng, 2023. Vol. 2023. P.3567194. doi: 10.1155/2023/3567194. 45. Ruiz-White I., Kramer L., Philips L., Wong B., Lonergan K., Moreno F. Racial and Ethnic Disparities in Physical and Mental Health Care and Clinical Trials. J Clin Psychiatry, 2023. Vol.84. I.4. A.23ah14887.P.1-11. doi: 10.4088/JCP.23ah14887. 46. Singhal A., Neveditsin N., Tanveer H., Mago V. Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review. JMIR Med Inform, 2024. Vol.12. P.e50048. doi: 10.2196/50048. 47. Spector-Bagdady K., Rahimzadeh V., Jaffe K., Moreno J. Promoting Ethical Deployment of Artificial Intelligence and Machine Learning in Healthcare. Am J Bioeth, 2022. Vol.22. I.5. P.4-7. doi: 10.1080/15265161.2022.2059206. 48. Sucasas Alonso A., Pértega Diaz S., Sáez Soto R., Avila-Alvarez A. Epidemiology and risk factors for bronchopulmonary dysplasia in preterm infants born at or less than 32 weeks of gestation. An Pediatr (Engl Ed), 2022. Vol.96. I.3. P.242-251. doi: 10.1016/j.anpede.2021.03.006. 49. Syder N.C., Elbuluk N. Racial and Ethnic Disparities in Research and Clinical Trials. Dermatol Clin, 2023. Vol.41. I.2. P.351-358. doi: 10.1016/j.det.2022.10.007. 50. Tang T.T., Zawaski J.A., Francis K.N., Qutub A.A., Gaber M.W. Image-based classification of tumor type and growth rate using machine learning: A preclinical study. Sci. Rep, 2019. Vol.9. I.1. P.12529. https://doi.org/10.1038/s41598-019-48738-5. 51. Tapuria A., Porat T., Kalra D., Dsouza G., Xiaohui S., Curcin V. Impact of patient access to their electronic health record: systematic review. Inform Health Soc Care, 2021. Vol.46. I.2. P.192-204. doi: 10.1080/17538157.2021.1879810. 52. Uffelmann E., Huang Q.Q., Munung N.S. et al. Genome-wide association studies// Nat Rev Methods Primers, 2021. Vol.1. P.59. https://doi.org/10.1038/s43586-021-00056-9. 53. Uslu A., Stausberg J. Value of the Electronic Medical Record for Hospital Care: Update From the Literature. J Med Internet Res, 2021. Vol.23. I.12. P.e26323. doi: 10.2196/26323. 54. Veinot T.C., Ancker J.S., Bakken S. Health informatics and health equity: improving our reach and impact. J Am Med Inform Assoc, 2019. Vol.26. P.689-695.doi: 10.1093/jamia/ocz132. 55. Xie F., Yuan H., Ning Y., Ong M.E.H., Feng M., Hsu W., Chakraborty B., Liu N. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies. J Biomed Inform, 2022. Vol.126. P.103980. doi: 10.1016/j.jbi.2021.103980. 56. Xing W., He W., Li X., Chen J., Cao Y., Zhou W., Shen Q., Zhang X., Ta D. Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation. Comput Methods Programs Biomed, 2022. Vol.221. P.106869. doi: 10.1016/j.cmpb.2022.106869. 57. Yang L., Zhang Y.H., Huang F., Li Z., Huang T., Cai Y.D. Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway. Front Genet, 2022. Vol.13. P.1011659. doi: 10.3389/fgene.2022.1011659. 58. Yusuf S.O., Chen P. Research Progress in Bronchopulmonary Dysplasia: A Narrative Review by Etiology. Open Journal of Pediatrics, 2022.Vol.12.P.554-568. https://doi.org/10.4236/ojped.2022.123058. 59. Zhou W., Shao F., Li J. Bioinformatic analysis of the molecular mechanism underlying bronchial pulmonary dysplasia using a text mining approach. Medicine, 2019. Vol. 98. P. e18493. doi: 10.1097/MD.0000000000018493

Number of Views: 37

Key words: bronchopulmonary dysplasia, biomedical informatics, genomics.

Category of articles: Reviews

Bibliography link

Abilbayeva A.A., Tarabayeva A.S. The role of biomedical informatics in the study of bronchopulmonary dysplasia // Nauka i Zdravookhranenie [Science & Healthcare]. 2024. Vol.26 (6), pp. 164-172. doi 10.34689/SH.2024.26.6.019