Paradigm shift required for translational research on the brain

참고문헌 (215)

1. Furuse, Y., "Analysis of research intensity on infectious disease by disease burden reveals which infectious diseases are neglected by researchers", Proc. Natl. Acad. Sci. USA, 116, pp.478~483 (2019)
2. Vehorn, C. L., "Measuring the contribution of biomedical research to the production of health", Res. Policy, 11, pp.3~113 (1982)
3. Adolphs, R., "The unsolved problems of neuroscience", Trends Cogn. Sci, 19, pp.173~175 (2015)
4. Lee, S. K., "The history of neuroscience 1 : ancient neuroscience", Epilia, 1, pp.4~10 (2019)
5. Tognoli, E., "Enlarging the scope : grasping brain complexity", Front. Syst. Neurosci, 8, pp.122~ (2014)
6. Premack, D., "Human and animal cognition : continuity and discontinuity", Proc. Natl. Acad. Sci. USA, 104, pp.13861~13867 (2007)
7. Jain, S., "Ethics in psychiatric research:issues and recommendations", Indian J. Psychol. Med, 39, pp.558~565 (2017)
8. Amunts, K., "Brain research challenges supercomputing", Science, 374, pp.1054~1055 (2021)
9. López-López, E., "Informatics for chemistry, biology, and biomedical sciences", J. Chem. Inf. Model, 61, pp.26~35 (2021)
10. Ogawa, S., "Intrinsic signal changes accompanying sensory stimulation : functional brain mapping with magnetic resonance imaging", Proc. Natl. Acad. Sci. USA, 89, pp.5951~5955 (1992)
11. Phelps, M. E., "Application of annihilation coincidence detection to transaxial reconstruction tomography", J. Nucl. Med, 16, pp.210~224 (1975)
12. Yang, J., "Metal–organic frameworks for biomedical applications", Small, 16, pp.e1906846~ (2020)
13. Duo, Y., "Borophene-based biomedical applications : status and future challenges", Coord. Chem. Rev, 427, pp.213549~ (2021)
14. Kisiala, A., "Quantification of cytokinins using high-resolution accurate-mass Orbitrap mass spectrometry and parallel reaction monitoring (PRM)", Anal. Chem, 91, pp.15049~15056 (2019)
15. Melfi, M. T., "Data processing for fennel protein characterization by Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR-MS)", Data Brief, 35, pp.106960~ (2021)
16. Schweppe, D. K., "Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics", J. Proteome Res, 19, pp.2026~2034 (2020)
17. Perez-Riverol, Y., "The PRIDE database resources in 2022 : a hub for mass spectrometry-based proteomics evidences", Nucleic Acids Res, 50, pp.D543~D552 (2022)
18. Bassett, D. S., "Understanding complexity in the human brain", Trends Cogn. Sci, 15, pp.200~209 (2011)
19. Poldrack, R. A., "Progress and challenges in probing the human brain", Nature, 526, pp.371~379 (2015)
20. Chen, S., "Exploring the interdisciplinary evolution of a discipline : the case of biochemistry and molecular biology", Sci entometrics, 102, pp.1307~1323 (2015)
21. Wen, J., "Many brains are better than one : the importance of interdisciplinary studies on COVID-19 in and beyond tourism", Tour. Recreat. Res, 46, pp.310~313 (2021)
22. Bloem, B. R., "Integrated and patient-centred management of Parkinson’s disease : a network model for reshaping chronic neurological care", Lancet Neurol, 19, pp.623~634 (2020)
23. Breasted, J. H., "The Edwin Smith Surgical Papyrus : published in facsimile and hieroglyphic transliteration with translation and commentary in two volumes", JAMA, 96, pp.1534~ (1931)
24. Simeone, F. A., "Andreas Vesalius : anatomist, surgeon, count palatine, and pilgrim", Am. J. Surg, 147, pp.432~440 (1984)
25. Piccolino, M., "Animal electricity and the birth of electrophysiology : the legacy of Luigi Galvani", Brain Res. Bull, 46, pp.381~407 (1998)
26. Fishman, M. C., "Sir Henry Hallett Dale and acetylcholine story", Yale J. Biol. Med, 45, pp.104~118 (1972)
27. Eccles, J. C., "Ionic mechanism of postsynaptic inhibition", Science, 145, pp.1140~1147 (1964)
28. Hell, S. W., "Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy", Opt. Lett, 19, pp.780~782 (1994)
29. Chojnacki, J., "Maturation-dependent HIV-1 surface protein redistribution revealed by fluorescence nanoscopy", Science, 338, pp.524~528 (2012)
30. Rust, M. J., "Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)", Nat. Methods, 3, pp.793~795 (2006)
31. Sahl, S. J., "Fluorescence nanoscopy in cell biology", Nat. Rev. Mol. Cell Biol, 18, pp.685~701 (2017)
32. Sidenstein, S. C., "Multicolour multilevel STED nanoscopy of actin", spectrin organization at synapses, 2016, pp.6, 26725~
33. Ning, K., "Classifying Alzheimer’s disease with brain imaging and genetic data using a neural network framework", Neurobiol. Aging, 68, pp.151~158 (2018)
34. Tian, W., "Meso-structure segmentation of concrete CT image based on mask and regional convolution neural network", Mater. Des, 208, pp.109919~ (2021)
35. Chung, K., "Structural and molecular interrogation of intact biological sys tems", Nature, 497, pp.332~337 (2013)
36. Chen, J. L., "Imaging neuronal populations in behaving rodents: paradigms for studying neural circuits underlying behavior in the mammalian cortex", J. Neurosci, 33, pp.17631~17640 (2013)
37. Lutz, A., "Regulation of the neural circuitry of emotion by compassion meditation : effects of meditative expertise", PLoS One, 3, pp.e1897~ (2008)
38. Marzluff, J. M., "Brain imaging reveals neuronal circuitry underlying the crow’s perception of human faces", Proc. Natl. Acad. Sci. USA, 109, pp.15912~15917 (2012)
39. Lauterbur, P. C., "Image formation by induced local interactions : examples employing nuclear magnetic resonance", Nature, 242, pp.190~191 (1973)
40. Bilezikian, J. P., "Hypoparathyroidism in the adult : epidemiology, diagnosis, pathophysiology, target-organ involvement, treatment, and challenges for future research", J. Bone Miner. Res, 26, pp.2317~2337 (2011)
41. Ten Kate, M., "Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline", Brain, 141, pp.3443~3456 (2018)
42. Maes, E., "Designing biomedical proteomics experiments : state-of-the-art and future perspectives", Expert Rev. Proteom, 13, pp.495~511 (2016)
43. Mishra, P., "Descriptive statistics and normality tests for statistical data", Ann. Card. Anaesth, 22, pp.67~72 (2019)
44. Wong, M., "Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer", Nat. Med, 26, pp.1742~1753 (2020)
45. Trapotsi, M. A., "Target Discovery and Validation: Methods and Strategies for Drug Discovery", pp.323~363 (2019), Wiley
46. Davis, R. L., "Mechanism of action and target identification : a matter of timing in drug discovery", Iscience, 23, pp.101487~ (2020)
47. pyrene case study, "Crit. Rev. Toxicol", 2015, 44~52
48. National Academies of Sciences, Engineering, and Medicine, "The clinical utility of compounded bioidentical hormone therapy: a review of safety, effectiveness, and use" (2020), National Academies Press
49. Brody, T., "Clinical trials: study design, endpoints and biomarkers, drug safety, and FDA and ICH guidelines" (2012), Elsevier
50. "The top 10 causes of death" (2020)[URL]
51. Foster, J. G., "Tradition and innovation in scientists’research strategies", Am. Sociol. Rev, 80, pp.875~908 (2015)
52. Li, D., "The applied value of public investments in biomedical research", Science, 356, pp.78~81 (2017)
53. Leenaars, C. H. C., "Animal to human translation : a systematic scoping review of reported concordance rates", J. Transl. Med, 17, pp.223~ (2019)
54. Olson, H., "Concordance of the toxicity of pharmaceuticals in humans and in animals", Regul. Toxicol. Pharmacol, 32, pp.56~67 (2000)
55. Yang, H. D., "History of Alzheimer’s disease", Dement. Neurocogn. Disord, 15, pp.115~121 (2016)
56. Goetz, C. G., "The history of Parkinson’s disease : early clinical descriptions and neurological therapies", Cold Spring Harb. Perspect. Med, 1, pp.a008862~ (2011)
57. McKee, M., "Book of the month", Priceless (2005)[DOI]
58. Buda, O., "Georges Marinesco and the early research in neuropathology", Neurology, 72, pp.88~91 (2009)
59. Goedert, M., "Oskar Fischer and the study of dementia", Brain, 132, pp.1102~1111 (2009)
60. Möckl, L., "Super‐resolved fluorescence microscopy:Nobel Prize in Chemistry 2014 for Eric Betzig, Stefan Hell, and William E. Moerner", Angew. Chem. Int. Ed. Engl, 53, pp.13972~13977 (2014)
61. Asher, S., "Alzheimer’s disease failed clinical trials", Life Sci, 306, pp.120861~ (2022)
62. Cummings, J., "Alzheimer’s drug-development pipeline:2016", Alzheimers Dement. (N. Y), 2, pp.222~232 (2016)
63. Cummings, J., "Alzheimer’s disease drug development pipeline: 2022", Alz heimers Dement. (N. Y), 8, pp.e12295~ (2022)
64. Wang, W., "Mitochondria dysfunction in the pathogenesis of Alzheimer’s disease : recent advances", Mol. Neurodegener, 15, pp.30~ (2020)
65. Leng, F., "Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here?", Nat. Rev. Neurol, 17, pp.157~172 (2021)
66. Liu, J., "The role of NMDA receptors in Alz heimer’s disease", Front. Neurosci, 13, pp.43~ (2019)
67. Bloem, B. R., "Parkinson’s disease", Lancet, 397, pp.2284~2303 (2021)
68. Mathur, S., "Neurodegenerative disorders: assessing the impact of natural vs drug‐induced treatment options", Aging Med. (Milton), 6, pp.82~97 (2023)
69. Cummings, J. L., "High-dose donepezil(23 mg", day)for the treatment of moderate and severe Alzheimer’s disease : drug profile and clinical guidelines, 2013, pp.19, 294~301
70. Orr, M. E., "A brief overview of tauopathy: causes, consequences, and therapeutic strategies", Trends Pharmacol. Sci, 38, pp.637~648 (2017)
71. Horstink, M., "Review of the therapeutic management of Parkinson’sdis ease. Report of a joint task force of the European Federation of Neurological Societies (EFNS) and the Movement Disorder Society‐European Section (MDS ES). Part II: late (complicated) Parkinson’s disease", Eur. J. Neurol, 13, pp.1186~1202 (2006)
72. Rascol, O., "Milestones in Parkinson’s disease therapeutics", Mov. Disord, 26, pp.1072~1082 (2011)
73. Bar-Or, A., "Teri f lunomide and its mechanism of action in multiple sclerosis", Drugs, 74, pp.659~674 (2014)
74. Eugena, K. G., "PSY 142—Abnormal Psychology" (2018), Hostos Community College
75. Ban, T. A., "Fifty years chlorpromazine : a historical perspective", Neuropsychiatr. Dis. Treat, 3, pp.495~500 (2007)
76. Laborit, H., "A new vegetative stabilizer; 4560 R.P", Presse Med. (1893), 60, pp.206~208 (1952)
77. Rosenbloom, M., "Chlorpromazine and the psychopharmacologic revolution", JAMA, 287, pp.1860~1861 (2002)
78. Seeman, P., "Antipsychotic drugs : direct correlation between clinical potency and presynaptic action on dopamine neurons", Science, 188, pp.1217~1219 (1975)
79. Baumgarten, H. G., "Role of serotonin in obsessive-compulsive disorder", Br. J. Psychiatry Suppl, 35, pp.13~20 (1998)
80. McTavish, D., "Clomipramine. An overview of its pharmacological properties and a review of its therapeutic use in obsessive compulsive disorder and panic disorder", Drugs, 39, pp.136~153 (1990)
81. Petty, F., "GABA and mood disorders : a brief review and hypothesis", J. Affect. Disord, 34, pp.275~281 (1995)
82. Fineberg, N. A., "The size, burden and cost of disorders of the brain in the UK", J. Psychopharmacol, 27, pp.761~770 (2013)
83. World Health Organization, "WHO guidelines Approved by the guidelines review committee", World Health Organization, ~ (2009)
84. Faraone, S. V., "Attention-deficit", hyperactivity disorder, 2015, pp.1, 15020~
85. Briars, L., "Areviewofpharmacological management of attention deficit", hyperactivity disorder, 2016, pp.21, 192~206
86. Carrey, N. J., "Pharmacological treatment of psy chiatric disorders in children and adolescents : focus on guidelines for the pri mary care practitioner", Drugs, 51, pp.750~759 (1996)
87. Charach, A., "Stimulant treatment over five years : adherence, effectiveness, and adverse effects", J. Am. Acad. Child Adolesc. Psy chiatry, 43, pp.559~567 (2004)
88. Groth, M., "Critical analysis of an e-learning and interactive teaching module with respect to the interpretation of emergency computed tomography of the brain", RoFo, 190, pp.334~340 (2018)
89. Kanner, A. M., "Antiseizure medications for adults with epilepsy : a review", JAMA, 327, pp.1269~1281 (2022)
90. Rho, J. M., "Brief history of anti-seizure drug development", Epilepsia Open, 3, pp.114~119 (2018)
91. Cano, A., "Epilepsy in neurodegenerative diseases: related drugs and molecular pathways", Pharmaceuticals(Basel), 14, pp.1057~ (2021)
92. Fan, H. C., "Epilepsy and attention deficit hyperactivity disorder : connection, chance, and challenges", Int. J. Mol. Sci, 24, pp.5270~ (2023)
93. Galovic, M., "Seizures and epilepsy after stroke : epidemiology, biomarkers and management", Drugs Aging, 38, pp.285~299 (2021)
94. Perucca, E., "The pharmacological treatment of epilepsy : recent advances and future perspectives", Acta Epileptol, 3, pp.22~ (2021)
95. Lally, J., "Antipsychotic medication in schizophrenia : a review", Br. Med. Bull, 114, pp.169~179 (2015)
96. Marder, S. R., "Schizophrenia", N. Engl. J. Med, 381, pp.1753~1761 (2019)
97. Pittenger, C., "Pharmacological treatment of obsessive-compulsive disorder", Psychiatr. Clin. North Am, 37, pp.375~391 (2014)
98. Fineberg, N., "Pharmacotherapy for obsessive—compulsive disorder", Adv. Psychiatr. Treat, 17, pp.419~434 (2011)
99. Carvalho, A. F., "Bipolar disorder", N. Engl. J. Med, 383, pp.58~66 (2020)
100. Tolou-Ghamari, Z., "A quick review of carbamazepine pharmacokinetics in epilepsy from 1953 to 2012", J. Res. Med. Sci, 18, pp.S81~S85 (2013)
101. "Therapeutic class overview attention deficit/hyperactivity disorder (ADHD)agents and stimulants" (2014)[URL]
102. Potnis, V. V., "A review on epilepsy and its management", J. Drug Deliv. Ther, 10, pp.273~279 (2020)
103. Richter, L., "Diazepam-bound GABAA receptor models identify new ben zodiazepine binding-site ligands", Nat. Chem. Biol, 8, pp.455~464 (2012)
104. Berger, H., "About human brain waves. Über das elektroenkephalogramm des menschen", Arch. f. Psychiatr, 87, pp.527~570 (1929)
105. Cohen, D., "Magnetoencephalography : evidence of magnetic fields produced by alpha-rhythm currents", Science, 161, pp.784~786 (1968)
106. Roy, C. S., "On the regulation of the blood-supply of the brain", J. Physiol, 11, pp.85~158 (1890)
107. Raichle, M. E., "A brief history of human brain mapping", Trends Neurosci, 32, pp.118~126 (2009)
108. Deco, G., "Rethinking segregation and integration : contributions of whole-brain modelling", Nat. Rev. Neurosci, 16, pp.430~439 (2015)
109. Marková, I. S., "Translational neuroscience and psychiatry : a conceptual analysis", J. Eval. Clin. Pract, 24, pp.791~796 (2018)
110. Wiegand, T. L. T., "Translational neuroimaging in mild traumatic brain injury", J. Neurosci. Res, 100, pp.1201~1217 (2022)
111. Bruce, E. D., "Neuroimaging and traumatic brain injury : state of the field and voids in translational knowledge", Mol. Cell. Neurosci, 66, pp.103~113 (2015)
112. Boroda, E., "Network topology changes in chronic mild traumatic brain injury(mTBI)", NeuroImage Clin, 31, pp.102691~ (2021)
113. To, X. V., "A road map of brain recovery in a mouse model of concussion : insights from neuroimaging", Acta Neuropathol. Commun, 9, pp.1~20 (2021)
114. Sudlow, C., "UK Biobank : an open access resource for identifying the causes of a wide range of complex diseases of middle and old age", PLOS Med, 12, pp.e1001779~ (2015)
115. Bethlehem, R. A. I., "Brain charts for the human lifespan", Nature, 604, pp.525~533 (2022)
116. Marek, S., "Reproducible brain-wide association studies require thousands of individuals", Nature, 603, pp.654~660 (2022)
117. Sauter, A. W., "Combined PET", MRI : one step further in multimodality imaging, 2010, pp.16, 508~515
118. Douaud, G., "Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia", Brain, 130, pp.2375~2386 (2007)
119. Watkins, K. E., "Structural and functional abnormalities of the motor system in developmental stuttering", Brain, 131, pp.50~59 (2008)
120. Mars, R. B., "Towards multi-modal, multi-species brain atlases: part one", Brain Struct. Funct, 228, pp.1041~1044 (2023)
121. Milham, M. P., "An open resource for non-human primate imaging", Neuron, 100, pp.61~74 (2018)
122. Suarez, L. E., "A connectomics-based taxonomy of mammals", eLife, 11, pp.e78635~ (2022)
123. Uludağ, K., "General overview on the merits of multimodal neuroimaging data fusion", Neuroimage, 102, pp.3~10 (2014)
124. Zhang, Y. D., "Advances in multimodal data fusion in neuroimaging : over view, challenges, and novel orientation", Inf. Fusion, 64, pp.149~187 (2020)
125. Tulay, E. E., "Multimodal neuroimaging : basic concepts and classification of neuropsychiatric diseases", Clin. EEG Neurosci, 50, pp.20~33 (2019)
126. Krasny, L., "Data-independent acquisition mass spectrometry(DIA MS)for proteomic applications in oncology", Mol. Omics, 17, pp.29~42 (2021)
127. Mann, M., "Proteomic analysis of post-translational modifications", Nat. Biotechnol, 21, pp.255~261 (2003)
128. Hasin, Y., "Multi-omics approaches to disease", Genome Biol, 18, pp.83~ (2017)
129. Vogel, C., "Insights into the regulation of protein abundance from proteomic and transcriptomic analyses", Nat. Rev. Genet, 13, pp.227~232 (2012)
130. Bersanelli, M., "Methods for the integration of multi-omics data : mathe matical aspects", BMC Bioinforma, 17, pp.15~ (2016)
131. Burla, B., "Ms-based lipidomics of human blood plasma : a community initiated position paper to develop accepted guidelines1", J. Lipid Res, 59, pp.2001~2017 (2018)
132. O’Donnell, V. B., "LIPID MAPS:serving the next generation of lipid researchers with tools, resources, data, and training", Sci. Signal, 12, pp.eaaw2964~ (2019)
133. Watrous, J. D., "Directed non-targeted mass spectrometry and chemical networking for discovery of eicosanoids and related oxylipins", Cell Chem. Biol, 26, pp.433~442 (2019)
134. Leier, H. C., "A global lipid map defines a network essential for Zika virus replication", Nat. Commun, 11, pp.3652~ (2020)
135. Tkachev, A., "Lipid alteration signature in the blood plasma of individuals with schizophrenia, depression, and bipolar disorder", JAMA Psychiatry, 80, pp.250~259 (2023)
136. Yoon, J. H., "Brain lipidomics: from functional landscape to clinical sig nificance", Sci. Adv, 8, pp.eadc9317~ (2022)
137. Chakraborty, S., "Onco-multi-OMICS approach : a new frontier in cancer research", BioMed. Res. Int, 2018, pp.9836256~ (2018)
138. Vasaikar, S. V., "LinkedOmics: analyzing multi omics data within and across 32 cancer types", Nucleic Acids Res, 46, pp.D956~D963 (2018)
139. Mun, D. G., "Proteogenomic characterization of human early-onset gastric cancer", Cancer Cell, 35, pp.111~124 (2019)
140. Zhang, H., "Integrated proteogenomic characterization of human high grade serous ovarian cancer", Cell, 166, pp.755~765 (2016)
141. Reska, D., "Integration of solutions and services for multi-omics data analysis towards personalized medicine", Biocybern. Biomed. Eng, 41, pp.1646~1663 (2021)
142. ICGC, "TCGA Pan-Cancer Analysis of Whole Genomes Consortium", Pan-cancer analysis of whole genomes, 2020, pp.578, 82~93
143. Cancer Genome Atlas Research Network, "The Cancer Genome Atlas Pan Cancer analysis project", Nat. Genet, 45, pp.1113~1120 (2013)
144. Lathrop, M., "International network of cancer genome projects(The Inter national Cancer Genome Consortium)", Nat. Dig, 464, pp.993~998 (2010)
145. Stein, L. D., "Data analysis : create a cloud commons", Nature, 523, pp.149~151 (2015)
146. Zhang, J., "The International Cancer Genome Consortium Data Portal", Nat. Biotechnol, 37, pp.367~369 (2019)
147. Ellis, M. J., "Connecting genomic alterations to cancer biology with pro teomics : the NCI Clinical Proteomic Tumor Analysis Consortium", Cancer Discov, 3, pp.1108~1112 (2013)
148. Whiteaker, J. R., "CPTAC Assay Portal : a repository of targeted proteomic assays", Nat. Methods, 11, pp.703~704 (2014)
149. Mertins, P., "Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry", Nat. Protoc, 13, pp.1632~1661 (2018)
150. Weiskirchen, S., "All you can feed: some comments on production of mouse diets used in biomedical research with special emphasis on non-alcoholic fatty liver disease research", Nutrients, 12, pp.163~ (2020)
151. Wu, P., "Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal*", Mol. Cell. Proteom, 18, pp.1893~1898 (2019)
152. Hu, Y., "Single cell multi-omics technology : methodology and application", Front. Cell Dev. Biol, 6, pp.28~ (2018)
153. Chappell, L., "Single-cell (multi)omics technologies", Annu. Rev. Genom. Hum. Genet, 19, pp.15~41 (2018)
154. Palazzotto, E., "Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms", Curr. Opin. Microbiol, 45, pp.109~116 (2018)
155. Ma, A., "Integrative methods and practical challenges for single-cell multi-omics", Trends Biotechnol, 38, pp.1007~1022 (2020)
156. Argelaguet, R., "Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets", Mol. Syst. Biol, 14, pp.e8124~ (2018)
157. Shen, R., "Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis", Bioinformatics, 25, pp.2906~2912 (2009)
158. Nicora, G., "Integrated multi-omics analyses in oncology : a review of machine learning methods and tools", Front. Oncol, 10, pp.1030~ (2020)
159. Zhou, G., "Network-based approaches for multi-omics integration", Methods Mol. Biol, 2104, pp.469~487 (2020)
160. Olivier, M., "The need for multi-omics biomarker signatures in precision medicine", Int. J. Mol. Sci, 20, pp.4781~ (2019)
161. Chute, C. G., "Some experiences and opportunities for big data in transla tional research", Genet. Med, 15, pp.802~809 (2013)
162. Onitsuka, T., "Trends in big data analyses by multicenter collaborative translational research in psychiatry", Psychiatry Clin. Neurosci, 76, pp.1~14 (2022)
163. Dunn, W. Jr, "Exploring and visualizing multidimensional data in translational research platforms", Brief. Bioinform, 18, pp.1044~1056 (2017)
164. van den Heuvel, M. P., "A spotlight on bridging microscale and macroscale human brain architecture", Neuron, 93, pp.1248~1251 (2017)
165. Almeida, S. A. Jr, "Review of artificial neural networks and a new feed forward network for anchorage analysis in cracked concrete", Int. Concr. Abstr. Portal, 350, pp.54~68 (2021)
166. Kaul, V., "History of artificial intelligence in medicine", Gastrointest. Endosc, 92, pp.807~812 (2020)
167. Jordan, M. I., "Machine learning: trends, perspectives, and prospects", Science, 349, pp.255~260 (2015)
168. Rajkomar, A., "Machine learning in medicine", N. Engl. J. Med, 380, pp.1347~1358 (2019)
169. Khalid, S., "A survey of feature selection and feature extraction techniques in machine learning", IEEE, pp.372~378 (2014)
170. Nogales, A., "A survey of deep learning models in medical therapeutic areas", Artif. Intell. Med, 112, pp.102020~ (2021)
171. Saxe, A., "If deep learning is the answer, what is the question?", Nat. Rev. Neurosci, 22, pp.5~67 (2021)
172. Yang, G. R., "Towards the next generation of recurrent network models for cognitive neuroscience", Curr. Opin. Neurobiol, 70, pp.182~192 (2021)
173. Bengio, Y., "Deep learning" (2016), The MIT Press
174. Komoroski, B., "Dapagliflozin, a novel SGLT2 inhibitor, induces dose dependent glucosuria in healthy subjects", Clin. Pharmacol. Ther, 85, pp.520~526 (2009)
175. Anderson, S. L., "Dapagliflozin for the treatment of type 2 diabetes", Ann. Pharmacother, 46, pp.590~598 (2012)
176. Agholi, M., "Microsporidia and coccidia as causes of persistence diarrhea among liver transplant children : incidence rate and species", genotypes, 2013, pp.32, 185~187
177. Anderson, S. L., "Dapagliflozin efficacy and safety : a perspective review", Ther. Adv. Drug Saf, 5, pp.242~254 (2014)
178. Azam, A., "Parasitic diarrheal disease: drug devel opment and targets", Front. Microbiol, 6, pp.1183~ (2015)
179. Miyamoto, S., "Transformation to small-cell lung cancer as a mechanism of acquired resistance to crizotinib and alectinib", Jpn. J. Clin. Oncol, 46, pp.170~173 (2016)
180. Dandona, P., "Minimizing glycemic fluctuations in patients with Type 2 diabetes : approaches and importance", Diabetes Technol. Ther, 19, pp.498~506 (2017)
181. Vamathevan, J., "Applications of machine learning in drug discovery and development", Nat. Rev. Drug Discov, 18, pp.463~477 (2019)
182. Lu, C., "Systemic evolutionary chemical space exploration for drug dis covery", J. Cheminform, 14, pp.19~ (2022)
183. Rifaioglu, A. S., "Recent applications of deep learning and machine intelli gence on in silico drug discovery : methods, tools and databases", Brief. Bioinform, 20, pp.1878~1912 (2019)
184. Goodfellow, I., "Generative adversarial nets", Adv. Neural Inf. Process. Syst, 27, pp.2672~2680 (2014)
185. Wang, R., "Applications of generative adversarial networks in neuroimaging and clinical neuroscience", Neuroimage, 269, pp.119898~ (2023)
186. Habashi, A. G., "Generative adversarial networks in EEG analysis : an overview", J. Neuroeng. Rehabil, 20, pp.40~ (2023)
187. Yu, W., "Tensorizing GAN with high-order pooling for Alzheimer’s disease assessment", IEEE Trans. Neural Netw. Learn. Syst, 33, pp.4945~4959 (2022)
188. Lin, W., "Bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer’s disease", Front. Neurosci, 15, pp.646013~ (2021)
189. Ahmed, K. T., "Multi-omics data integration by generative adversarial network", Bioinformatics, 38, pp.179~186 (2021)
190. Ai, X., "Generative adversarial networks applied to gene expression analysis : an interdisciplinary perspective", Comp. Syst. Oncol, 3, pp.e1050~ (2023)
191. Khot, U. N., "Prevalence of conventional risk factors in patients with cor onary heart disease", JAMA, 290, pp.898~904 (2003)
192. Payne, M., "The Routledge Handbook of Social Work Theory" (2019), Taylor and Francis Group
193. Ressler, K. J., "Big data in psychiatry : multiomics, neuroimaging, computational modeling, and digital phenotyping", Neuropsychopharmacology, 46, pp.1~2 (2021)
194. Tan, M. S., "A review on omics based biomarkers discovery for Alzheimer’s disease from the bioinformatics perspectives : statistical approach vs machine learning approach", Comput. Biol. Med, 139, pp.104947~ (2021)
195. Termine, A., "Multi-layer picture of neurodegenerative diseases : lessons from the use of big data through artificial intelligence", J. Pers. Med, 11, pp.280~ (2021)
196. Macarron, R., "Impact of high-throughput screening in biomedical research", Nat. Rev. Drug Discov, 10, pp.188~195 (2011)
197. Manouchehri, E., "Domestic violence experienced by women with multiple sclerosis : a study from the North-East of Iran", BMC Women’s Health, 22, pp.321~ (2022)
198. Rahman, M. M., "Biomedical research in developing countries : opportunities, methods, and challenges", Indian J. Gastroenterol, 39, pp.292~302 (2020)
199. Sun, J., "Isolation of infectious SARS-CoV-2 from urine of a COVID-19 patient", Emerg. Microbes Infect, 9, pp.991~993 (2020)
201. Kurvits, L., "Transcriptomic profiles in Parkinson’s disease", Exp. Biol. Med. (Maywood), 246, pp.584~595 (2021)
202. Li, X., "Review of metabolomics-based biomarker research for Parkinson’s disease", Mol. Neurobiol, 59, pp.1041~1057 (2022)
203. Boutet, A., "Predicting optimal deep brain stimulation parameters for Par kinson’s disease using functional MRI and machine learning", Nat. Commun, 12, pp.3043~ (2021)
204. Elkouzi, A., "Emerging therapies in Parkinson disease—repurposed drugs and new approaches", Nat. Rev. Neurol, 15, pp.204~223 (2019)
205. Chen, J., "Unifying diagnosis identification and pre diction method embedding the disease ontology structure from electronic medical records", Front. Public Health, 9, pp.793801~ (2021)
206. Myszczynska, M. A., "Applications of machine learning to diagnosis and treatment of neurodegenerative diseases", Nat. Rev. Neurol, 16, pp.440~456 (2020)
207. Park, S. W., "Machine learning application for classification of Alzheimer’s disease stages using 18F-flortaucipir positron emission tomography", Biomed. Eng. OnLine, 22, pp.40~ (2023)
208. Simfukwe, C., "Classification of Abeta State from brain amyloid PET images using machine learning algorithm", Dement. Neurocogn. Disord, 22, pp.61~68 (2023)
209. Kim, N. H., "PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease", Sci. Rep, 13, pp.10299~ (2023)
210. Brickman, A. M., "Correlation of plasma and neuroimaging biomarkers in Alzheimer’s disease", Ann. Clin. Transl. Neurol, 9, pp.756~761 (2022)
211. Chan, Y. H., "Combining neuroimaging and omics datasets for disease classification using graph neural networks", Front. Neurosci, 16, pp.866666~ (2022)
212. Rayaprolu, S., "Systems-based proteomics to resolve the biology of Alz heimer’s disease beyond amyloid and tau", Neuropsychopharmacology, 46, pp.98~115 (2021)
213. Adewale, Q., "Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer’s disease", eLife, 10, pp.e62589~ (2021)
214. Vogel, J. W., "Connectome-based modelling of neurodegenerative diseases : towards precision medicine and mechanistic insight", Nat. Rev. Neurosci, 24, pp.620~639 (2023)
215. Guo, X., "Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder", Mol. Autism, 13, pp.52~ (2022)

관련 논문AI추천