{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T11:45:44Z","timestamp":1781869544406,"version":"3.54.5"},"reference-count":112,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"DOI":"10.1007\/s12021-025-09729-2","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T11:47:14Z","timestamp":1752148034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing"],"prefix":"10.1007","volume":"23","author":[{"given":"Phani","family":"Paladugu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jahnavi","family":"Yelamanchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Waisberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mouayad","family":"Masalkhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8177-2784","authenticated-orcid":false,"given":"Chirag","family":"Gowda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dylan","family":"Amiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Jagadeesan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasif","family":"Zaman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Tavakkoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew G.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"issue":"1","key":"9729_CR1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s13321-022-00623-6","volume":"14","author":"M Abbasi","year":"2022","unstructured":"Abbasi, M., Santos, B. P., Pereira, T. C., Sofia, R., Monteiro, N. R. C., Sim\u00f5es, C. J. V., Brito, R. M. M., Ribeiro, B., Oliveira, J. L., & Arrais, J. P. (2022). Designing optimized drug candidates with Generative Adversarial Network. Journal of Cheminformatics, 14(1), 40. https:\/\/doi.org\/10.1186\/s13321-022-00623-6. Erratum.In:JCheminform.2022Aug11;14(1):53.10.1186\/s13321-022-00631-6.","journal-title":"Journal of Cheminformatics"},{"key":"9729_CR2","doi-asserted-by":"publisher","unstructured":"Albrecht LJ, H\u00f6wner A, Griewank K, et al (2022) Circulating cell-free messenger RNA enables non-invasive pan-tumour monitoring of melanoma therapy independent of the mutational genotype. Clinical and Translational Medicine 12. https:\/\/doi.org\/10.1002\/ctm2.1090","DOI":"10.1002\/ctm2.1090"},{"issue":"2","key":"9729_CR3","doi-asserted-by":"publisher","first-page":"641","DOI":"10.3390\/s24020641","volume":"24","author":"L Alhoraibi","year":"2024","unstructured":"Alhoraibi, L., Alghazzawi, D., & Alhebshi, R. (2024). Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer Authentication. Sensors (Basel), 24(2), 641. https:\/\/doi.org\/10.3390\/s24020641","journal-title":"Sensors (Basel)"},{"issue":"9","key":"9729_CR4","doi-asserted-by":"publisher","first-page":"840262","DOI":"10.3389\/fcvm.2022.840262","volume":"27","author":"S Amal","year":"2022","unstructured":"Amal, S., Safarnejad, L., Omiye, J. A., Ghanzouri, I., Cabot, J. H., & Ross, E. G. (2022). Use of multi-modal data and machine learning to improve cardiovascular disease care. Frontiers in Cardiovascular Medicine, 27(9), 840262. https:\/\/doi.org\/10.3389\/fcvm.2022.840262","journal-title":"Frontiers in Cardiovascular Medicine"},{"issue":"2","key":"9729_CR5","doi-asserted-by":"publisher","first-page":"190","DOI":"10.7861\/fhj.2022-0013","volume":"9","author":"A Arora","year":"2022","unstructured":"Arora, A. (2022). Generative adversarial networks and synthetic patient data: Current challenges and future perspectives. Future Healthcare Journal, 9(2), 190\u2013193. https:\/\/doi.org\/10.7861\/fhj.2022-0013","journal-title":"Future Healthcare Journal"},{"issue":"4","key":"9729_CR6","doi-asserted-by":"publisher","first-page":"e043497","DOI":"10.1136\/bmjopen-2020-043497","volume":"11","author":"Z Azizi","year":"2021","unstructured":"Azizi, Z., Zheng, C., Mosquera, L., Pilote, L., & El Emam, K. (2021). GOING-FWD Collaborators. Can synthetic data be a proxy for real clinical trial data? A validation study. British Medical Journal Open, 11(4), e043497. https:\/\/doi.org\/10.1136\/bmjopen-2020-043497","journal-title":"British Medical Journal Open"},{"key":"9729_CR7","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-1-60327-241-4_13","volume":"609","author":"A Ben-Hur","year":"2010","unstructured":"Ben-Hur, A., & Weston, J. (2010). A user\u2019s guide to support vector machines. Methods in Molecular Biology, 609, 223\u2013239. https:\/\/doi.org\/10.1007\/978-1-60327-241-4_13","journal-title":"Methods in Molecular Biology"},{"key":"9729_CR8","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1093\/ije\/dyz223","volume":"49","author":"MS Bannick","year":"2021","unstructured":"Bannick, M. S., McGaughey, M., & Flaxman, A. D. (2021). Ensemble modelling in descriptive epidemiology: Burden of disease estimation. International Journal of Epidemiology, 49, 2065\u20132073. https:\/\/doi.org\/10.1093\/ije\/dyz223","journal-title":"International Journal of Epidemiology"},{"key":"9729_CR9","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s12028-023-01691-8","volume":"39","author":"S Brasil","year":"2023","unstructured":"Brasil, S., de Carvalho, N. R., Salinet, \u00c2. S. M., et al. (2023). Critical closing pressure and cerebrovascular resistance responses to intracranial pressure variations in neurocritical patients. Neurocritical Care, 39, 399\u2013410. https:\/\/doi.org\/10.1007\/s12028-023-01691-8","journal-title":"Neurocritical Care"},{"issue":"2","key":"9729_CR10","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1002\/alz.13518","volume":"20","author":"WS Brum","year":"2024","unstructured":"Brum, W. S., Ashton, N. J., Simr\u00e9n, J., di Molfetta, G., Karikari, T. K., Benedet, A. L., Zimmer, E. R., Lantero-Rodriguez, J., Montoliu-Gaya, L., Jeromin, A., Aarsand, A. K., Bartlett, W. A., Calle, P. F., Co\u015fkun, A., D\u00edaz-Garz\u00f3n, J., Jonker, N., Zetterberg, H., Sandberg, S., Carobene, A., & Blennow, K. (2024). Biological variation estimates of Alzheimer\u2019s disease plasma biomarkers in healthy individuals. Alzheimer\u2019s & Dementia, 20(2), 1284\u20131297. https:\/\/doi.org\/10.1002\/alz.13518","journal-title":"Alzheimer's & Dementia"},{"key":"9729_CR11","doi-asserted-by":"publisher","unstructured":"Burgos K, Malenica I, Metpally R, et al (2014) Profiles of extracellular miRNA in cerebrospinal fluid and serum from patients with Alzheimer's and Parkinson's diseases correlate with disease status and features of pathology. PLoS One 9. https:\/\/doi.org\/10.1371\/journal.pone.0094839","DOI":"10.1371\/journal.pone.0094839"},{"issue":"3","key":"9729_CR12","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1002\/jmri.26695","volume":"50","author":"R Burman","year":"2019","unstructured":"Burman, R., Shah, A. H., Benveniste, R., Jimsheleishvili, G., Lee, S. H., Loewenstein, D., & Alperin, N. (2019). Comparing invasive with MRI-derived intracranial pressure measurements in healthy elderly and brain trauma cases: A pilot study. Journal of Magnetic Resonance Imaging, 50(3), 975\u2013981. https:\/\/doi.org\/10.1002\/jmri.26695","journal-title":"Journal of Magnetic Resonance Imaging"},{"issue":"39","key":"9729_CR13","doi-asserted-by":"publisher","first-page":"100647","DOI":"10.1016\/j.pacs.2024.100647","volume":"12","author":"Y Cao","year":"2024","unstructured":"Cao, Y., Li, Y., Fu, W., Cheng, G., Tian, X., Wang, J., Zha, S., & Wang, J. (2024). High performance filtering and high-sensitivity concentration retrieval of methane in photoacoustic spectroscopy utilizing deep learning residual networks. Photoacoustics, 12(39), 100647. https:\/\/doi.org\/10.1016\/j.pacs.2024.100647","journal-title":"Photoacoustics"},{"issue":"5","key":"9729_CR14","doi-asserted-by":"publisher","first-page":"605","DOI":"10.3748\/wjg.v28.i5.605","volume":"28","author":"P Charilaou","year":"2022","unstructured":"Charilaou, P., & Battat, R. (2022). Machine learning models and over-fitting considerations. World Journal of Gastroenterology, 28(5), 605\u2013607. https:\/\/doi.org\/10.3748\/wjg.v28.i5.605","journal-title":"World Journal of Gastroenterology"},{"issue":"6","key":"9729_CR15","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","volume":"5","author":"RJ Chen","year":"2021","unstructured":"Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021). Synthetic data in machine learning for medicine and healthcare. Nature Biomedical Engineering, 5(6), 493\u2013497. https:\/\/doi.org\/10.1038\/s41551-021-00751-8","journal-title":"Nature Biomedical Engineering"},{"key":"9729_CR16","doi-asserted-by":"publisher","unstructured":"Chen, S., Zhang, C., & Mu, H. (2024a). An adaptive learning rate deep learning optimizer using long and short-term gradients based on G-L fractional-order derivative. Neural Processing Letters, 56, 106. https:\/\/doi.org\/10.1007\/s11063-024-11571-7","DOI":"10.1007\/s11063-024-11571-7"},{"key":"9729_CR17","doi-asserted-by":"publisher","unstructured":"Chen, H., Dan, L., Lu, Y., Chen, M., & Zhang, J. (2024b). An improved data augmentation approach and its application in medical named entity recognition. BMC Medical Informatics and Decision Making, 24(1), 221. https:\/\/doi.org\/10.1186\/s12911-024-02624-x","DOI":"10.1186\/s12911-024-02624-x"},{"key":"9729_CR18","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1080\/14737159.2019.1633307","volume":"19","author":"KWE Cheung","year":"2019","unstructured":"Cheung, K. W. E., Choi, S. R., Lee, L. T. C., et al. (2019). The potential of circulating cell free RNA as a biomarker in cancer. Expert Review of Molecular Diagnostics, 19, 579\u2013590. https:\/\/doi.org\/10.1080\/14737159.2019.1633307","journal-title":"Expert Review of Molecular Diagnostics"},{"issue":"7","key":"9729_CR19","doi-asserted-by":"publisher","first-page":"e623","DOI":"10.7717\/peerj-cs.623","volume":"5","author":"D Chicco","year":"2021","unstructured":"Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj Computer Science, 5(7), e623. https:\/\/doi.org\/10.7717\/peerj-cs.623","journal-title":"Peerj Computer Science"},{"issue":"1","key":"9729_CR20","doi-asserted-by":"publisher","first-page":"e0010061","DOI":"10.1371\/journal.pntd.0010061","volume":"16","author":"SR da Silva Neto","year":"2022","unstructured":"da Silva Neto, S. R., Tabosa Oliveira, T., Teixeira, I. V., Aguiar de Oliveira, S. B., Souza Sampaio, V., Lynn, T., & Endo, P. T. (2022). Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review. PLoS Neglected Tropical Diseases, 16(1), e0010061. https:\/\/doi.org\/10.1371\/journal.pntd.0010061","journal-title":"PLoS Neglected Tropical Diseases"},{"key":"9729_CR21","doi-asserted-by":"publisher","first-page":"e2300021","DOI":"10.1200\/CCI.23.00021","volume":"7","author":"S D'Amico","year":"2023","unstructured":"D\u2019Amico, S., Dall\u2019Olio, D., Sala, C., Dall\u2019Olio, L., Sauta, E., Zampini, M., Asti, G., Lanino, L., Maggioni, G., Campagna, A., Ubezio, M., Russo, A., Bicchieri, M. E., Riva, E., Tentori, C. A., Travaglino, E., Morandini, P., Savevski, V., Santoro, A., \u2026 Della Porta, M. G. (2023). Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology. JCO Clinical Cancer Informatics, 7, e2300021. https:\/\/doi.org\/10.1200\/CCI.23.00021","journal-title":"JCO Clinical Cancer Informatics"},{"issue":"1","key":"9729_CR22","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1186\/s13195-022-01113-5","volume":"14","author":"EB Dammer","year":"2022","unstructured":"Dammer, E. B., Ping, L., Duong, D. M., Modeste, E. S., Seyfried, N. T., Lah, J. J., Levey, A. I., & Johnson, E. C. B. (2022). Multi-platform proteomic analysis of Alzheimer\u2019s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome. Alzheimer\u2019s Research & Therapy, 14(1), 174. https:\/\/doi.org\/10.1186\/s13195-022-01113-5","journal-title":"Alzheimer's Research & Therapy"},{"key":"9729_CR23","doi-asserted-by":"publisher","unstructured":"D'Antona L, Asif H, Craven CL, et al. (2021) Brain MRI and ophthalmic biomarkers of intracranial pressure. Neurology 96\u2013e2723. https:\/\/doi.org\/10.1212\/WNL.0000000000012023","DOI":"10.1212\/WNL.0000000000012023"},{"issue":"11","key":"9729_CR24","doi-asserted-by":"publisher","first-page":"e23665","DOI":"10.1002\/ajhb.23665","volume":"34","author":"AM DeLouize","year":"2022","unstructured":"DeLouize, A. M., Eick, G., Karam, S. D., & Snodgrass, J. J. (2022). Current and future applications of biomarkers in samples collected through minimally invasive methods for cancer medicine and population-based research. American Journal of Human Biology, 34(11), e23665. https:\/\/doi.org\/10.1002\/ajhb.23665","journal-title":"American Journal of Human Biology"},{"issue":"1","key":"9729_CR25","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1186\/s13244-022-01315-3","volume":"13","author":"A Dimitriadis","year":"2022","unstructured":"Dimitriadis, A., Trivizakis, E., Papanikolaou, N., Tsiknakis, M., & Marias, K. (2022). Enhancing cancer differentiation with synthetic MRI examinations via generative models: A systematic review. Insights into Imaging, 13(1), 188. https:\/\/doi.org\/10.1186\/s13244-022-01315-3","journal-title":"Insights into Imaging"},{"issue":"16","key":"9729_CR26","doi-asserted-by":"publisher","first-page":"1025492","DOI":"10.3389\/fnins.2022.1025492","volume":"9","author":"PMN Dos Santos","year":"2023","unstructured":"Dos Santos, P. M. N., Mendes, S. L., Biazoli, C., Gadelha, A., Salum, G. A., Miguel, E. C., Rohde, L. A., & Sato, J. R. (2023). Assessing atypical brain functional connectivity development: An approach based on generative adversarial networks. Frontiers in Neuroscience, 9(16), 1025492. https:\/\/doi.org\/10.3389\/fnins.2022.1025492","journal-title":"Frontiers in Neuroscience"},{"issue":"3","key":"9729_CR27","doi-asserted-by":"publisher","first-page":"e02510","DOI":"10.7554\/eLife.02510","volume":"27","author":"NH Du","year":"2014","unstructured":"Du, N. H., Arpat, A. B., De Matos, M., & Gatfield, D. (2014). MicroRNAs shape circadian hepatic gene expression on a transcriptome-wide scale. eLife, 27(3), e02510. https:\/\/doi.org\/10.7554\/eLife.02510","journal-title":"eLife"},{"issue":"1","key":"9729_CR28","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1186\/s40537-021-00516-9","volume":"8","author":"T Emmanuel","year":"2021","unstructured":"Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big Data, 8(1), 140. https:\/\/doi.org\/10.1186\/s40537-021-00516-9","journal-title":"Journal of Big Data"},{"key":"9729_CR29","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.dadm.2017.04.007","volume":"8","author":"S Engelborghs","year":"2017","unstructured":"Engelborghs, S., Niemantsverdriet, E., Struyfs, H., et al. (2017). Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement (Amst), 8, 111\u2013126. https:\/\/doi.org\/10.1016\/j.dadm.2017.04.007","journal-title":"Alzheimers Dement (Amst)"},{"key":"9729_CR30","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s12987-020-00195-3","volume":"17","author":"KB Evensen","year":"2020","unstructured":"Evensen, K. B., & Eide, P. K. (2020). Measuring intracranial pressure by invasive, less invasive or non-invasive means: Limitations and avenues for improvement. Fluids Barriers CNS, 17, 34. https:\/\/doi.org\/10.1186\/s12987-020-00195-3","journal-title":"Fluids Barriers CNS"},{"key":"9729_CR31","doi-asserted-by":"publisher","first-page":"517","DOI":"10.3357\/AMHP.5922.2022","volume":"93","author":"H F\u00e9lix","year":"2022","unstructured":"F\u00e9lix, H., & Oliveira, E. S. (2022). Non-invasive intracranial pressure monitoring and its applicability in spaceflight. Aerospace Medicine and Human Performance, 93, 517\u2013531. https:\/\/doi.org\/10.3357\/AMHP.5922.2022","journal-title":"Aerospace Medicine and Human Performance"},{"issue":"2","key":"9729_CR32","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3969\/j.issn.1002-0829.2014.02.009","volume":"26","author":"C Feng","year":"2014","unstructured":"Feng, C., Wang, H., Lu, N., Chen, T., He, H., Lu, Y., & Tu, X. M. (2014). Log-transformation and its implications for data analysis. Shanghai Archives of Psychiatry, 26(2), 105\u2013109. https:\/\/doi.org\/10.3969\/j.issn.1002-0829.2014.02.009. Erratum.In:GenPsychiatr.2019Sep6;32(5):e100146corr1.10.1136\/gpsych-2019-100146corr1.","journal-title":"Shanghai Archives of Psychiatry"},{"key":"9729_CR33","doi-asserted-by":"publisher","first-page":"101935","DOI":"10.1016\/j.artmed.2020.101935","volume":"108","author":"L Gao","year":"2020","unstructured":"Gao, L., Zhang, L., Liu, C., & Wu, S. (2020). Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artificial Intelligence in Medicine, 108, 101935. https:\/\/doi.org\/10.1016\/j.artmed.2020.101935","journal-title":"Artificial Intelligence in Medicine"},{"issue":"3","key":"9729_CR34","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1038\/s42256-023-00629-1","volume":"5","author":"C Gao","year":"2023","unstructured":"Gao, C., Killeen, B. D., Hu, Y., Grupp, R. B., Taylor, R. H., Armand, M., & Unberath, M. (2023). Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis. Nature Machine Intelligence, 5(3), 294\u2013308. https:\/\/doi.org\/10.1038\/s42256-023-00629-1","journal-title":"Nature Machine Intelligence"},{"issue":"11","key":"9729_CR35","doi-asserted-by":"publisher","first-page":"585804","DOI":"10.3389\/fgene.2020.585804","volume":"11","author":"Q Ge","year":"2020","unstructured":"Ge, Q., Huang, X., Fang, S., Guo, S., Liu, Y., Lin, W., & Xiong, M. (2020). Conditional Generative Adversarial Networks for Individualized Treatment Effect Estimation and Treatment Selection. Frontiers in Genetics, 11(11), 585804. https:\/\/doi.org\/10.3389\/fgene.2020.585804","journal-title":"Frontiers in Genetics"},{"issue":"7","key":"9729_CR36","doi-asserted-by":"publisher","first-page":"4141","DOI":"10.1007\/s12035-022-02822-6","volume":"59","author":"HS Ghaith","year":"2022","unstructured":"Ghaith, H. S., Nawar, A. A., Gabra, M. D., Abdelrahman, M. E., Nafady, M. H., Bahbah, E. I., Ebada, M. A., Ashraf, G. M., Negida, A., & Barreto, G. E. (2022). A Literature Review of Traumatic Brain Injury Biomarkers. Molecular Neurobiology, 59(7), 4141\u20134158. https:\/\/doi.org\/10.1007\/s12035-022-02822-6","journal-title":"Molecular Neurobiology"},{"issue":"1","key":"9729_CR37","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/s41746-023-00927-3","volume":"6","author":"M Giuffr\u00e8","year":"2023","unstructured":"Giuffr\u00e8, M., & Shung, D. L. (2023). Harnessing the power of synthetic data in healthcare: Innovation, application, and privacy. NPJ Digital Medicine, 6(1), 186. https:\/\/doi.org\/10.1038\/s41746-023-00927-3","journal-title":"NPJ Digital Medicine"},{"issue":"1","key":"9729_CR38","doi-asserted-by":"publisher","first-page":"5417","DOI":"10.1038\/s41467-024-49094-3","volume":"15","author":"G Glehr","year":"2024","unstructured":"Glehr, G., Riquelme, P., Kronenberg, K., Lohmayer, R., L\u00f3pez-Madrona, V. J., Kapinsky, M., Schlitt, H. J., Geissler, E. K., Spang, R., Haferkamp, S., & Hutchinson, J. A. (2024). Restricting datasets to classifiable samples augments discovery of immune disease biomarkers. Nature Communications, 15(1), 5417. https:\/\/doi.org\/10.1038\/s41467-024-49094-3","journal-title":"Nature Communications"},{"key":"9729_CR39","doi-asserted-by":"publisher","unstructured":"Glinge C, Clauss S, Boddum K, et al (2017) Stability of circulating blood-based microRNAs - pre-analytic methodological considerations. PLoS One 12. https:\/\/doi.org\/10.1371\/journal.pone.0167969","DOI":"10.1371\/journal.pone.0167969"},{"key":"9729_CR40","doi-asserted-by":"publisher","first-page":"2908","DOI":"10.1038\/s41467-024-47286-5","volume":"15","author":"F Gonzalez-Ortiz","year":"2024","unstructured":"Gonzalez-Ortiz, F., Kirsebom, B. E., Contador, J., et al. (2024). Plasma brain-derived tau is an amyloid-associated neurodegeneration biomarker in Alzheimer\u2019s disease. Nature Communications, 15, 2908. https:\/\/doi.org\/10.1038\/s41467-024-47286-5","journal-title":"Nature Communications"},{"key":"9729_CR41","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-211-85578-2_85","volume":"102","author":"JC Goodman","year":"2008","unstructured":"Goodman, J. C., Van, M., Gopinath, S. P., & Robertson, C. S. (2008). Pro-inflammatory and pro-apoptotic elements of the neuroinflammatory response are activated in traumatic brain injury. Acta Neurochirurgica. Supplementum, 102, 437\u2013439. https:\/\/doi.org\/10.1007\/978-3-211-85578-2_85","journal-title":"Acta Neurochirurgica. Supplementum"},{"issue":"2","key":"9729_CR42","doi-asserted-by":"publisher","first-page":"2251830","DOI":"10.1080\/21645515.2023.2251830","volume":"19","author":"JP Gygi","year":"2023","unstructured":"Gygi, J. P., Kleinstein, S. H., & Guan, L. (2023). Predictive overfitting in immunological applications: Pitfalls and solutions. Human Vaccines & Immunotherapeutics, 19(2), 2251830. https:\/\/doi.org\/10.1080\/21645515.2023.2251830","journal-title":"Human Vaccines & Immunotherapeutics"},{"issue":"4","key":"9729_CR43","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1080\/00401706.2020.1791959","volume":"62","author":"T Hastie","year":"2020","unstructured":"Hastie, T. (2020). Ridge Regularization: An Essential Concept in Data Science. Technometrics, 62(4), 426\u2013433. https:\/\/doi.org\/10.1080\/00401706.2020.1791959","journal-title":"Technometrics"},{"key":"9729_CR44","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.neunet.2023.08.063","volume":"167","author":"L He","year":"2023","unstructured":"He, L., Ai, Q., Yang, X., Ren, Y., Wang, Q., & Xu, Z. (2023). Boosting adversarial robustness via self-paced adversarial training. Neural Networks, 167, 706\u2013714. https:\/\/doi.org\/10.1016\/j.neunet.2023.08.063","journal-title":"Neural Networks"},{"key":"9729_CR45","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1097\/IJG.0000000000001293","volume":"28","author":"AS Huang","year":"2019","unstructured":"Huang, A. S., Stenger, M. B., & Macias, B. R. (2019). Gravitational influence on intraocular pressure: Implications for spaceflight and disease. Journal of Glaucoma, 28, 756\u2013764. https:\/\/doi.org\/10.1097\/IJG.0000000000001293","journal-title":"Journal of Glaucoma"},{"issue":"18","key":"9729_CR46","doi-asserted-by":"publisher","first-page":"1333712","DOI":"10.3389\/fnins.2024.1333712","volume":"15","author":"N Huynh","year":"2024","unstructured":"Huynh, N., & Deshpande, G. (2024). A review of the applications of generative adversarial networks to structural and functional MRI based diagnostic classification of brain disorders. Frontiers in Neuroscience, 15(18), 1333712. https:\/\/doi.org\/10.3389\/fnins.2024.1333712","journal-title":"Frontiers in Neuroscience"},{"issue":"133","key":"9729_CR47","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1152\/japplphysiol.00625.2021","volume":"2022","author":"JV Jasien","year":"1985","unstructured":"Jasien, J. V., Laurie, S. S., Lee, S. M. C., et al. (1985). Noninvasive indicators of intracranial pressure before, during, and after long-duration spaceflight. Journal of Applied Physiology, 2022(133), 721\u2013731. https:\/\/doi.org\/10.1152\/japplphysiol.00625.2021","journal-title":"Journal of Applied Physiology"},{"issue":"14","key":"9729_CR48","doi-asserted-by":"publisher","first-page":"3608","DOI":"10.3390\/cancers15143608","volume":"15","author":"X Jiang","year":"2023","unstructured":"Jiang, X., Hu, Z., Wang, S., & Zhang, Y. (2023). Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel), 15(14), 3608. https:\/\/doi.org\/10.3390\/cancers15143608","journal-title":"Cancers (Basel)"},{"key":"9729_CR49","doi-asserted-by":"publisher","unstructured":"Jiang Y, Garc\u00eda-Dur\u00e1n A, Losada IB, Girard P, Terranova N. (2024) Generative models for synthetic data generation: application to pharmacokinetic\/pharmacodynamic data. Journal Of Pharmacokinetics And Pharmacodynamics https:\/\/doi.org\/10.1007\/s10928-024-09935-6","DOI":"10.1007\/s10928-024-09935-6"},{"key":"9729_CR50","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41526-024-00364-w","volume":"10","author":"SA Kamran","year":"2024","unstructured":"Kamran, S. A., Hossain, K. F., Ong, J., et al. (2024). SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data. Npj Microgravity, 10, 40. https:\/\/doi.org\/10.1038\/s41526-024-00364-w","journal-title":"Npj Microgravity"},{"key":"9729_CR51","doi-asserted-by":"publisher","unstructured":"Kang HYJ, Batbaatar E, Choi DW, et al (2023) Synthetic tabular data based on generative adversarial networks in health care: generation and validation using the divide-and-conquer strategy. JMIR Medical Informatics 11. https:\/\/doi.org\/10.2196\/47859","DOI":"10.2196\/47859"},{"issue":"101","key":"9729_CR52","doi-asserted-by":"publisher","first-page":"20140933","DOI":"10.1098\/rsif.2014.0933","volume":"11","author":"CK Kang","year":"2014","unstructured":"Kang, C. K., & Shyy, W. (2014). Analytical model for instantaneous lift and shape deformation of an insect-scale flapping wing in hover. Journal of the Royal Society, Interface, 11(101), 20140933. https:\/\/doi.org\/10.1098\/rsif.2014.0933","journal-title":"Journal of the Royal Society, Interface"},{"key":"9729_CR53","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.neunet.2018.09.009","volume":"110","author":"SH Khan","year":"2019","unstructured":"Khan, S. H., Hayat, M., & Porikli, F. (2019). Regularization of deep neural networks with spectral dropout. Neural Networks, 110, 82\u201390. https:\/\/doi.org\/10.1016\/j.neunet.2018.09.009","journal-title":"Neural Networks"},{"issue":"4","key":"9729_CR54","doi-asserted-by":"publisher","first-page":"e0284791","DOI":"10.1371\/journal.pone.0284791","volume":"18","author":"F Khan","year":"2023","unstructured":"Khan, F., Yu, X., Yuan, Z., & Rehman, A. U. (2023). ECG classification using 1-D convolutional deep residual neural network. PLoS ONE, 18(4), e0284791. https:\/\/doi.org\/10.1371\/journal.pone.0284791","journal-title":"PLoS ONE"},{"key":"9729_CR55","doi-asserted-by":"publisher","first-page":"93","DOI":"10.47936\/encephalitis.2022.00045","volume":"2","author":"KT Kim","year":"2022","unstructured":"Kim, K. T. (2022). Lumbar puncture: Considerations, procedure, and complications. Encephalitis, 2, 93\u201397. https:\/\/doi.org\/10.47936\/encephalitis.2022.00045","journal-title":"Encephalitis"},{"issue":"3","key":"9729_CR56","doi-asserted-by":"publisher","first-page":"282","DOI":"10.3390\/clockssleep2030022","volume":"2","author":"C Kinoshita","year":"2020","unstructured":"Kinoshita, C., Okamoto, Y., Aoyama, K., & Nakaki, T. (2020). MicroRNA: A key player for the interplay of circadian rhythm abnormalities, sleep disorders and neurodegenerative diseases. Clocks Sleep., 2(3), 282\u2013307. https:\/\/doi.org\/10.3390\/clockssleep2030022","journal-title":"Clocks Sleep."},{"issue":"1","key":"9729_CR57","doi-asserted-by":"publisher","first-page":"e0243915","DOI":"10.1371\/journal.pone.0243915","volume":"16","author":"V Kunc","year":"2021","unstructured":"Kunc, V., & Kl\u00e9ma, J. (2021). On transformative adaptive activation functions in neural networks for gene expression inference. PLoS ONE, 16(1), e0243915. https:\/\/doi.org\/10.1371\/journal.pone.0243915","journal-title":"PLoS ONE"},{"key":"9729_CR58","doi-asserted-by":"publisher","first-page":"104436","DOI":"10.1016\/j.jbi.2023.104436","volume":"144","author":"NI Kuo","year":"2023","unstructured":"Kuo, N. I., Garcia, F., S\u00f6nnerborg, A., et al. (2023). Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV. Journal of Biomedical Informatics, 144, 104436. https:\/\/doi.org\/10.1016\/j.jbi.2023.104436","journal-title":"Journal of Biomedical Informatics"},{"issue":"3","key":"9729_CR59","doi-asserted-by":"publisher","first-page":"e20201795","DOI":"10.1084\/jem.20201795","volume":"218","author":"N Kyritsis","year":"2021","unstructured":"Kyritsis, N., Torres-Esp\u00edn, A., Schupp, P. G., Huie, J. R., Chou, A., Duong-Fernandez, X., Thomas, L. H., Tsolinas, R. E., Hemmerle, D. D., Pascual, L. U., Singh, V., Pan, J. Z., Talbott, J. F., Whetstone, W. D., Burke, J. F., DiGiorgio, A. M., Weinstein, P. R., Manley, G. T., Dhall, S. S., \u2026 Beattie, M. S. (2021). Diagnostic blood RNA profiles for human acute spinal cord injury. Journal of Experimental Medicine, 218(3), e20201795. https:\/\/doi.org\/10.1084\/jem.20201795","journal-title":"Journal of Experimental Medicine"},{"issue":"3","key":"9729_CR60","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/s1047-2797(02)00261-2","volume":"13","author":"D Lai","year":"2003","unstructured":"Lai, D., King, T. M., Moy\u00e9, L. A., & Wei, Q. (2003). Sample size for biomarker studies: More subjects or more measurements per subject? Annals of Epidemiology, 13(3), 204\u2013208. https:\/\/doi.org\/10.1016\/s1047-2797(02)00261-2","journal-title":"Annals of Epidemiology"},{"issue":"8","key":"9729_CR61","doi-asserted-by":"publisher","first-page":"164","DOI":"10.3389\/fpubh.2020.00164","volume":"12","author":"L Lan","year":"2020","unstructured":"Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y., & Zhou, X. (2020). Generative Adversarial Networks and Its Applications in Biomedical Informatics. Frontiers in Public Health, 12(8), 164. https:\/\/doi.org\/10.3389\/fpubh.2020.00164","journal-title":"Frontiers in Public Health"},{"key":"9729_CR62","doi-asserted-by":"publisher","unstructured":"Lan H; Alzheimer Disease Neuroimaging Initiative; Toga AW, Sepehrband F. (2021) Three-dimensional self-attention conditional GAN with spectral normalization for multimodal neuroimaging synthesis. Magnetic Resonance In Medicine 86(3):1718\u20131733. https:\/\/doi.org\/10.1002\/mrm.28819","DOI":"10.1002\/mrm.28819"},{"issue":"6","key":"9729_CR63","doi-asserted-by":"publisher","first-page":"503","DOI":"10.4097\/kja.20137","volume":"73","author":"DK Lee","year":"2020","unstructured":"Lee, D. K. (2020). Data transformation: A focus on the interpretation. Korean Journal of Anesthesiology, 73(6), 503\u2013508. https:\/\/doi.org\/10.4097\/kja.20137","journal-title":"Korean Journal of Anesthesiology"},{"key":"9729_CR64","unstructured":"Leike, J., Wu, J., Bills, S., Saunders, W., Gao, L., Tillman, H., Mossing, D. (2023). Language models can explain neurons in language models. OpenAI. https:\/\/openai.com\/index\/language-models-can-explain-neurons-in-language-models\/. Accessed 1 Jul 2025."},{"issue":"9","key":"9729_CR65","doi-asserted-by":"publisher","first-page":"424","DOI":"10.3390\/genes9090424","volume":"9","author":"X Li","year":"2018","unstructured":"Li, X., Li, W., & Xu, Y. (2018). Human age prediction based on dna methylation using a gradient boosting regressor. Genes (Basel), 9(9), 424. https:\/\/doi.org\/10.3390\/genes9090424","journal-title":"Genes (Basel)"},{"issue":"12","key":"9729_CR66","doi-asserted-by":"publisher","first-page":"9629","DOI":"10.1109\/TPAMI.2021.3127558","volume":"44","author":"C Li","year":"2022","unstructured":"Li, C., Xu, K., Zhu, J., Liu, J., & Zhang, B. (2022). Triple Generative Adversarial Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 9629\u20139640. https:\/\/doi.org\/10.1109\/TPAMI.2021.3127558","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"8","key":"9729_CR67","doi-asserted-by":"publisher","first-page":"205520762211344","DOI":"10.1177\/20552076221134455","volume":"27","author":"TJ Loftus","year":"2022","unstructured":"Loftus, T. J., Ruppert, M. M., Shickel, B., Ozrazgat-Baslanti, T., Balch, J. A., Efron, P. A., Upchurch, G. R., Jr., Rashidi, P., Tignanelli, C., Bian, J., & Bihorac, A. (2022). Federated learning for preserving data privacy in collaborative healthcare research. Digital Healt, 27(8), 20552076221134456. https:\/\/doi.org\/10.1177\/20552076221134455","journal-title":"Digital Healt"},{"issue":"12","key":"9729_CR68","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https:\/\/doi.org\/10.1186\/s13059-014-0550-8","journal-title":"Genome Biology"},{"issue":"3","key":"9729_CR69","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.cell.2012.10.012","volume":"151","author":"J Lov\u00e9n","year":"2012","unstructured":"Lov\u00e9n, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012). Revisiting global gene expression analysis. Cell, 151(3), 476\u2013482. https:\/\/doi.org\/10.1016\/j.cell.2012.10.012","journal-title":"Cell"},{"key":"9729_CR70","doi-asserted-by":"publisher","first-page":"1808","DOI":"10.3390\/healthcare11121808","volume":"11","author":"P Mahajan","year":"2023","unstructured":"Mahajan, P., Uddin, S., Hajati, F., et al. (2023). Ensemble learning for disease prediction: A review. Healthcare (Basel), 11, 1808. https:\/\/doi.org\/10.3390\/healthcare11121808","journal-title":"Healthcare (Basel)"},{"key":"9729_CR71","doi-asserted-by":"publisher","first-page":"105","DOI":"10.2147\/EB.S234076","volume":"12","author":"Y Martin Paez","year":"2020","unstructured":"Martin Paez, Y., Mudie, L. I., & Subramanian, P. S. (2020). Spaceflight associated neuro-ocular syndrome (SANS): A systematic review and future directions. Eye Brain, 12, 105\u2013117. https:\/\/doi.org\/10.2147\/EB.S234076","journal-title":"Eye Brain"},{"issue":"25","key":"9729_CR72","doi-asserted-by":"publisher","first-page":"e50638","DOI":"10.2196\/50638","volume":"4","author":"B Mesk\u00f3","year":"2023","unstructured":"Mesk\u00f3, B. (2023). Prompt Engineering as an Important Emerging Skill for Medical Professionals: Tutorial. Journal of Medical Internet Research, 4(25), e50638. https:\/\/doi.org\/10.2196\/50638","journal-title":"Journal of Medical Internet Research"},{"issue":"15","key":"9729_CR73","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.3390\/cancers13153819","volume":"13","author":"C Molinaro","year":"2021","unstructured":"Molinaro, C., Martoriati, A., & Cailliau, K. (2021). Proteins from the DNA damage response: Regulation, dysfunction, and anticancer strategies. Cancers (Basel), 13(15), 3819. https:\/\/doi.org\/10.3390\/cancers13153819","journal-title":"Cancers (Basel)"},{"issue":"9","key":"9729_CR74","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1136\/jnnp-2015-311302","volume":"87","author":"SP Mollan","year":"2016","unstructured":"Mollan, S. P., Ali, F., Hassan-Smith, G., Botfield, H., Friedman, D. I., & Sinclair, A. J. (2016). Evolving evidence in adult idiopathic intracranial hypertension: Pathophysiology and management. Journal of Neurology, Neurosurgery & Psychiatry, 87(9), 982\u2013992. https:\/\/doi.org\/10.1136\/jnnp-2015-311302","journal-title":"Journal of Neurology, Neurosurgery & Psychiatry"},{"key":"9729_CR75","doi-asserted-by":"publisher","unstructured":"Montesinos L\u00f3pez OA, Montesinos L\u00f3pez A, Crossa J. Multivariate Statistical Machine Learning Methods for Genomic Prediction [Internet]. Cham (CH): Springer; 2022. Chapter 10, Fundamentals of Artificial Neural Networks and Deep Learning. 2022 Jan 14. Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK583971\/https:\/\/doi.org\/10.1007\/978-3-030-89010-0_10","DOI":"10.1007\/978-3-030-89010-0_10"},{"issue":"6","key":"9729_CR76","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.3390\/jcm12062209","volume":"12","author":"SJ M\u00fcller","year":"2023","unstructured":"M\u00fcller, S. J., Henkes, E., Gounis, M. J., Felber, S., Ganslandt, O., & Henkes, H. (2023). Non-Invasive Intracranial Pressure Monitoring. Journal of Clinical Medicine, 12(6), 2209. https:\/\/doi.org\/10.3390\/jcm12062209","journal-title":"Journal of Clinical Medicine"},{"key":"9729_CR77","unstructured":"Munakomi, S., Das, J. M. (2024). Intracranial pressure monitoring. In: StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL). Available from: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK542298\/.\u00a010\/10\/2024."},{"issue":"8","key":"9729_CR78","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.3390\/ijerph18084287","volume":"18","author":"J Musulin","year":"2021","unstructured":"Musulin, J., Baressi\u0160egota, S., \u0160tifani\u0107, D., Lorencin, I., An\u0111eli\u0107, N., \u0160u\u0161ter\u0161i\u010d, T., Blagojevi\u0107, A., Filipovi\u0107, N., \u0106abov, T., & Markova-Car, E. (2021). Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. International Journal of Environmental Research and Public Health, 18(8), 4287. https:\/\/doi.org\/10.3390\/ijerph18084287","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"11","key":"9729_CR79","first-page":"521","volume":"2024","author":"B Naderalvojoud","year":"2023","unstructured":"Naderalvojoud, B., & Hernandez-Boussard, T. (2023b). Improving machine learning with ensemble learning on observational healthcare data. AMIA Annual Symposium Proceedings, 2024(11), 521\u2013529.","journal-title":"AMIA Annual Symposium Proceedings"},{"key":"9729_CR80","first-page":"521","volume":"2023","author":"B Naderalvojoud","year":"2024","unstructured":"Naderalvojoud, B., & Hernandez-Boussard, T. (2024a). Improving machine learning with ensemble learning on observational healthcare data. American Medical Informatics Association Annual Symposium Proceedings, 2023, 521\u2013529.","journal-title":"American Medical Informatics Association Annual Symposium Proceedings"},{"key":"9729_CR81","doi-asserted-by":"publisher","first-page":"10811","DOI":"10.3390\/ijerph182010811","volume":"18","author":"DK Nguyen","year":"2021","unstructured":"Nguyen, D. K., Lan, C. H., & Chan, C. L. (2021). Deep ensemble learning approaches in healthcare to enhance the prediction and diagnosing performance: The workflows, deployments, and surveys on the statistical, image-based, and sequential datasets. International Journal of Environmental Research and Public Health, 18, 10811. https:\/\/doi.org\/10.3390\/ijerph182010811","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"1","key":"9729_CR82","doi-asserted-by":"publisher","first-page":"19290","DOI":"10.1038\/s41598-022-23242-5","volume":"12","author":"O Oladipo","year":"2022","unstructured":"Oladipo, O., Omidiora, E. O., & Osamor, V. C. (2022). A novel genetic-artificial neural network based age estimation system. Science and Reports, 12(1), 19290. https:\/\/doi.org\/10.1038\/s41598-022-23242-5","journal-title":"Science and Reports"},{"key":"9729_CR83","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/s41526-022-00222-7","volume":"8","author":"J Ong","year":"2022","unstructured":"Ong, J., Tavakkoli, A., Zaman, N., et al. (2022). Terrestrial health applications of visual assessment technology and machine learning in spaceflight associated neuro-ocular syndrome. NPJ Microgravity, 8, 37. https:\/\/doi.org\/10.1038\/s41526-022-00222-7","journal-title":"NPJ Microgravity"},{"issue":"6","key":"9729_CR84","doi-asserted-by":"publisher","first-page":"106849","DOI":"10.1016\/j.isci.2023.106849","volume":"26","author":"M Orf","year":"2023","unstructured":"Orf, M., W\u00f6stmann, M., Hannemann, R., & Obleser, J. (2023). Target enhancement but not distractor suppression in auditory neural tracking during continuous speech. iScience, 26(6), 106849. https:\/\/doi.org\/10.1016\/j.isci.2023.106849","journal-title":"iScience"},{"key":"9729_CR85","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1007\/s10439-023-03304-z","volume":"51","author":"PS Paladugu","year":"2023","unstructured":"Paladugu, P. S., Ong, J., Nelson, N., et al. (2023). Generative adversarial networks in medicine: Important considerations for this emerging innovation in artificial intelligence. Annals of Biomedical Engineering, 51, 2130\u20132142. https:\/\/doi.org\/10.1007\/s10439-023-03304-z","journal-title":"Annals of Biomedical Engineering"},{"key":"9729_CR86","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.lssr.2024.03.007","volume":"42","author":"P Paladugu","year":"2024","unstructured":"Paladugu, P., Ong, J., Kumar, R., Waisberg, E., Zaman, N., Kamran, S. A., Tavakkoli, A., Rivolta, M. C., Nelson, N., Yoo, T., Douglas, V. P., Douglas, K., Song, A., Tso, H., & Lee, A. G. (2024). Lower body negative pressure as a research tool and countermeasure for the physiological effects of spaceflight: A comprehensive review. Life Sciences in Space Research (Amst), 42, 8\u201316. https:\/\/doi.org\/10.1016\/j.lssr.2024.03.007","journal-title":"Life Sciences in Space Research (Amst)"},{"issue":"6","key":"9729_CR87","doi-asserted-by":"publisher","first-page":"1410947","DOI":"10.3389\/fdgth.2024.1410947","volume":"12","author":"YHPP Priyadarshana","year":"2024","unstructured":"Priyadarshana, Y. H. P. P., Senanayake, A., Liang, Z., & Piumarta, I. (2024). Prompt engineering for digital mental health: A short review. Frontiers in Digital Health, 12(6), 1410947. https:\/\/doi.org\/10.3389\/fdgth.2024.1410947","journal-title":"Frontiers in Digital Health"},{"key":"9729_CR88","doi-asserted-by":"publisher","first-page":"950393","DOI":"10.1155\/2012\/950393","volume":"2012","author":"PH Raboel","year":"2012","unstructured":"Raboel, P. H., Bartek, J., Jr., Andresen, M., Bellander, B. M., & Romner, B. (2012). Intracranial pressure monitoring: Invasive versus non-invasive methods\u2014a review. Crit Care Res Pract, 2012, 950393. https:\/\/doi.org\/10.1155\/2012\/950393","journal-title":"Crit Care Res Pract"},{"key":"9729_CR89","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/s41698-022-00270-y","volume":"6","author":"B Roskams-Hieter","year":"2022","unstructured":"Roskams-Hieter, B., Kim, H. J., Anur, P., et al. (2022). Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies. NPJ Precis Oncol, 6, 28. https:\/\/doi.org\/10.1038\/s41698-022-00270-y","journal-title":"NPJ Precis Oncol"},{"issue":"1","key":"9729_CR90","doi-asserted-by":"publisher","first-page":"23299","DOI":"10.1038\/s41598-024-74291-x","volume":"14","author":"B Sarker","year":"2024","unstructured":"Sarker, B., Chakraborty, S., \u010cep, R., & Kalita, K. (2024). Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes. Science and Reports, 14(1), 23299. https:\/\/doi.org\/10.1038\/s41598-024-74291-x","journal-title":"Science and Reports"},{"issue":"14","key":"9729_CR91","doi-asserted-by":"publisher","first-page":"604559","DOI":"10.3389\/fnmol.2021.604559","volume":"10","author":"P Shichkova","year":"2021","unstructured":"Shichkova, P., Coggan, J. S., Markram, H., & Keller, D. (2021). A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation. Frontiers in Molecular Neuroscience, 10(14), 604559. https:\/\/doi.org\/10.3389\/fnmol.2021.604559","journal-title":"Frontiers in Molecular Neuroscience"},{"key":"9729_CR92","doi-asserted-by":"publisher","unstructured":"Sohel Md Mahmodul (2016) Extracellular\/circulating microRNAs: release mechanisms, functions and challenges. Achievements in the Life Sciences 10. https:\/\/doi.org\/10.1016\/j.als.2016.11.007","DOI":"10.1016\/j.als.2016.11.007"},{"key":"9729_CR93","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1089\/neu.2007.0300","volume":"24","author":"N Stocchetti","year":"2007","unstructured":"Stocchetti, N., Colombo, A., Ortolano, F., et al. (2007). Time course of intracranial hypertension after traumatic brain injury. Journal of Neurotrauma, 24, 1339\u20131346. https:\/\/doi.org\/10.1089\/neu.2007.0300","journal-title":"Journal of Neurotrauma"},{"key":"9729_CR94","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neunet.2018.01.016","volume":"101","author":"T Takase","year":"2018","unstructured":"Takase, T., Oyama, S., & Kurihara, M. (2018). Effective neural network training with adaptive learning rate based on training loss. Neural Networks, 101, 68\u201378. https:\/\/doi.org\/10.1016\/j.neunet.2018.01.016","journal-title":"Neural Networks"},{"issue":"5","key":"9729_CR95","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.cels.2019.04.003","volume":"8","author":"JN Taroni","year":"2019","unstructured":"Taroni, J. N., Grayson, P. C., Hu, Q., Eddy, S., Kretzler, M., Merkel, P. A., & Greene, C. S. (2019). MultiPLIER: A transfer learning framework for transcriptomics reveals systemic features of rare disease. Cell Systems, 8(5), 380-394.e4. https:\/\/doi.org\/10.1016\/j.cels.2019.04.003","journal-title":"Cell Systems"},{"issue":"19","key":"9729_CR96","doi-asserted-by":"publisher","first-page":"5977","DOI":"10.1158\/1078-0432.CCR-07-4534","volume":"14","author":"JM Taylor","year":"2008","unstructured":"Taylor, J. M., Ankerst, D. P., & Andridge, R. R. (2008). Validation of biomarker-based risk prediction models. Clinical Cancer Research, 14(19), 5977\u20135983. https:\/\/doi.org\/10.1158\/1078-0432.CCR-07-4534","journal-title":"Clinical Cancer Research"},{"key":"9729_CR97","doi-asserted-by":"publisher","first-page":"105046","DOI":"10.1016\/j.ebiom.2024.105046","volume":"102","author":"J Therriault","year":"2024","unstructured":"Therriault, J., Ashton, N. J., Pola, I., Triana-Baltzer, G., Brum, W. S., Di Molfetta, G., et al. (2024). Comparison of two plasma p-tau217 assays to detect and monitor Alzheimer\u2019s pathology. eBioMedicine, 102, 105046. https:\/\/doi.org\/10.1016\/j.ebiom.2024.105046","journal-title":"eBioMedicine"},{"key":"9729_CR98","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1038\/nature21365","volume":"542","author":"T Thomou","year":"2017","unstructured":"Thomou, T., Mori, M. A., Dreyfuss, J. M., et al. (2017). Adipose-derived circulating miRNAs regulate gene expression in other tissues. Nature, 542, 450\u2013455. https:\/\/doi.org\/10.1038\/nature21365","journal-title":"Nature"},{"issue":"8","key":"9729_CR99","doi-asserted-by":"publisher","first-page":"924","DOI":"10.3390\/biom14080924","volume":"14","author":"BA Tripp","year":"2024","unstructured":"Tripp, B. A., Dillon, S. T., Yuan, M., Asara, J. M., Vasunilashorn, S. M., Fong, T. G., Inouye, S. K., Ngo, L. H., Marcantonio, E. R., Xie, Z., Libermann, T. A., & Otu, H. H. (2024). Integrated Multi-Omics Analysis of Cerebrospinal Fluid in Postoperative Delirium. Biomolecules, 14(8), 924. https:\/\/doi.org\/10.3390\/biom14080924","journal-title":"Biomolecules"},{"issue":"3","key":"9729_CR100","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1007\/s11030-021-10225-3","volume":"25","author":"JM Vaz","year":"2021","unstructured":"Vaz, J. M., & Balaji, S. (2021). Convolutional neural networks (CNNs): Concepts and applications in pharmacogenomics. Molecular Diversity, 25(3), 1569\u20131584. https:\/\/doi.org\/10.1007\/s11030-021-10225-3","journal-title":"Molecular Diversity"},{"issue":"1","key":"9729_CR101","doi-asserted-by":"publisher","first-page":"bbab431","DOI":"10.1093\/bib\/bbab431","volume":"23","author":"CC Wang","year":"2022","unstructured":"Wang, C. C., Zhu, C. C., & Chen, X. (2022). Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. Briefings in Bioinformatics, 23(1), bbab431. https:\/\/doi.org\/10.1093\/bib\/bbab431","journal-title":"Briefings in Bioinformatics"},{"issue":"1","key":"9729_CR102","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41746-024-01029-4","volume":"7","author":"L Wang","year":"2024","unstructured":"Wang, L., Chen, X., Deng, X., Wen, H., You, M., Liu, W., Li, Q., & Li, J. (2024). Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs. NPJ Digital Medicine, 7(1), 41. https:\/\/doi.org\/10.1038\/s41746-024-01029-4","journal-title":"NPJ Digital Medicine"},{"key":"9729_CR103","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1097\/WCO.0000000000000778","volume":"33","author":"P Wojcik","year":"2020","unstructured":"Wojcik, P., Kini, A., Al Othman, B., Galdamez, L. A., & Lee, A. G. (2020). Spaceflight associated neuro-ocular syndrome. Current Opinion in Neurology, 33, 62\u201367. https:\/\/doi.org\/10.1097\/WCO.0000000000000778","journal-title":"Current Opinion in Neurology"},{"key":"9729_CR104","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/s41433-021-01721-9","volume":"36","author":"P Wostyn","year":"2022","unstructured":"Wostyn, P., Gibson, C. R., & Mader, T. H. (2022). The odyssey of the ocular and cerebrospinal fluids during a mission to Mars: The \u201cocular glymphatic system\u201d under pressure. Eye (London, England), 36, 686\u2013691. https:\/\/doi.org\/10.1038\/s41433-021-01721-9","journal-title":"Eye (London, England)"},{"issue":"15","key":"9729_CR105","doi-asserted-by":"publisher","first-page":"2779","DOI":"10.2147\/COPD.S271237","volume":"4","author":"J Xia","year":"2020","unstructured":"Xia, J., Sun, L., Xu, S., Xiang, Q., Zhao, J., Xiong, W., Xu, Y., & Chu, S. (2020). A model using support vector machines recursive feature elimination (SVM-RFE) algorithm to classify whether COPD patients have been continuously managed according to GOLD guidelines. International Journal of Chronic Obstructive Pulmonary Disease, 4(15), 2779\u20132786. https:\/\/doi.org\/10.2147\/COPD.S271237","journal-title":"International Journal of Chronic Obstructive Pulmonary Disease"},{"issue":"6","key":"9729_CR106","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1097\/01.mat.0000249015.76446.40","volume":"52","author":"S Yang","year":"2006","unstructured":"Yang, S., Ji, B., Undar, A., & Zahn, J. D. (2006). Microfluidic devices for continuous blood plasma separation and analysis during pediatric cardiopulmonary bypass procedures. ASAIO Journal, 52(6), 698\u2013704. https:\/\/doi.org\/10.1097\/01.mat.0000249015.76446.40","journal-title":"ASAIO Journal"},{"issue":"6","key":"9729_CR107","doi-asserted-by":"publisher","first-page":"1430245","DOI":"10.3389\/fdgth.2024.1430245","volume":"26","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Khorshidi, H. A., & Aickelin, U. (2024). A review on over-sampling techniques in classification of multi-class imbalanced datasets: Insights for medical problems. Frontiers in Digital Health, 26(6), 1430245. https:\/\/doi.org\/10.3389\/fdgth.2024.1430245","journal-title":"Frontiers in Digital Health"},{"key":"9729_CR108","doi-asserted-by":"publisher","unstructured":"Zanello, S. B., Tadigotla, V., Hurley, J., Skog, J., Stevens, B., Calvillo, E., Bershad, E. (2018). Inflammatory gene expression signatures in idiopathic intracranial hypertension: Possible implications in microgravity-induced ICP elevation. NPJ Microgravity, 4, 1. https:\/\/doi.org\/10.1038\/s41526-017-0036-6","DOI":"10.1038\/s41526-017-0036-6"},{"key":"9729_CR109","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1080\/14737159.2018.1425143","volume":"18","author":"IA Zaporozhchenko","year":"2018","unstructured":"Zaporozhchenko, I. A., Ponomaryova, A. A., Rykova, E. Y., et al. (2018). The potential of circulating cell-free RNA as a cancer biomarker: Challenges and opportunities. Expert Review of Molecular Diagnostics, 18, 133\u2013145. https:\/\/doi.org\/10.1080\/14737159.2018.1425143","journal-title":"Expert Review of Molecular Diagnostics"},{"issue":"4","key":"9729_CR110","doi-asserted-by":"publisher","first-page":"100050","DOI":"10.1016\/j.patter.2020.100050","volume":"1","author":"C Zhang","year":"2020","unstructured":"Zhang, C., Wang, J., Yen, G. G., Zhao, C., Sun, Q., Tang, Y., Qian, F., & Kurths, J. (2020). When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey. Patterns (n y), 1(4), 100050. https:\/\/doi.org\/10.1016\/j.patter.2020.100050","journal-title":"Patterns (n y)"},{"key":"9729_CR111","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.3390\/ijms17081232","volume":"17","author":"M Zhou","year":"2016","unstructured":"Zhou, M., Hara, H., Dai, Y., et al. (2016). Circulating organ-specific microRNAs serve as biomarkers in organ-specific diseases: Implications for organ allo- and xeno-transplantation. International Journal of Molecular Sciences, 17, 1232. https:\/\/doi.org\/10.3390\/ijms17081232","journal-title":"International Journal of Molecular Sciences"},{"issue":"1","key":"9729_CR112","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s41746-024-01290-7","volume":"7","author":"Z Zhou","year":"2024","unstructured":"Zhou, Z., Guo, Y., Tang, R., Liang, H., He, J., & Xu, F. (2024). Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis. NPJ Digital Medicine, 7(1), 293. https:\/\/doi.org\/10.1038\/s41746-024-01290-7","journal-title":"NPJ Digital Medicine"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-025-09729-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-025-09729-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-025-09729-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T04:06:09Z","timestamp":1760587569000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-025-09729-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":112,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["9729"],"URL":"https:\/\/doi.org\/10.1007\/s12021-025-09729-2","relation":{},"ISSN":["1559-0089"],"issn-type":[{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,10]]},"assertion":[{"value":"22 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. This study was a secondary analysis of publicly available NASA datasets (Open Science Data Repository 363\u2013364) and did not involve direct experimentation on human or animal subjects. No IRB approval or consent to participate\/publish was required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Andrew G. Lee has received compensation as a speaker for Amgen and Alexion and has served as a consultant for Viridian, Erythreal, Catalyst, AstraZeneca, Bristol Myers Squibb, Stoke, and the U.S. Department of Justice. He is also a consultant for NASA; however, the views expressed in this study are his own and do not necessarily reflect those of NASA or the U.S. government.  Ram Jagadeesan is an employee of Cisco and holds stock in the company.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"38"}}