[1] Zhang R, Simon G, Yu F. Advancing Alzheimer's research:a review of big data promises[J]. Int J Med Inform, 2017, 106:48-56.
[2] Gong MC, Lu L. Research progress and application prospect of medical big data[J]. Yi Xue Xin Xi Xue Za Zhi, 2016, 37:9-15.[弓孟春, 陆亮. 医学大数据研究进展及应用前景[J]. 医学信息学杂志, 2016, 37:9-15.]
[3] Fan Y, Feng M, Wang R. Application of radiomics in central nervous system diseases:a systematic literature review[J]. Clin Neurol Neurosurg, 2019, 187:105565.
[4] Abul-Husn NS, Kenny EE. Personalized medicine and the power of electronic health records[J]. Cell, 2019, 177:58-69.
[5] Qiao N, Shen M, He W, He M, Zhang Z, Ye H, Li Y, Shou X, Li S, Jiang C, Wang Y, Zhao Y. Machine learning in predicting early remission in patients after surgical treatment of acromegaly:a multicenter study[J]. Pituitary, 2021, 24:53-61.
[6] Fan Y, Li Y, Bao X, Zhu H, Lu L, Yao Y, Li Y, Su M, Feng F, Feng S, Feng M, Wang R. Development of machine learning models for predicting postoperative delayed remission in patients with Cushing's disease[J]. J Clin Endocrinol Metab, 2021, 106:e217-231.
[7] Qiao N. A systematic review on machine learning in sellar region diseases:quality and reporting items[J]. Endocr Connect, 2019, 8:952-960.
[8] Kogan E, Twyman K, Heap J, Milentijevic D, Lin JH, Alberts M. Assessing stroke severity using electronic health record data:a machine learning approach[J]. BMC Med Inform Decis Mak, 2020, 20:8.
[9] Guan W, Ko D, Khurshid S, Trisini Lipsanopoulos AT, Ashburner JM, Harrington LX, Rost NS, Atlas SJ, Singer DE, McManus DD, Anderson CD, Lubitz SA. Automated electronic phenotyping of cardioembolic stroke[J]. Stroke, 2021, 52:181-189.
[10] Li L, Ruau D, Chen R, Weber S, Butte AJ. Systematic identification of risk factors for Alzheimer's disease through shared genetic architecture and electronic medical records[J]. Pac Symp Biocomput, 2013:224-235.
[11] Parikh M, Hynan LS, Weiner MF, Lacritz L, Ringe W, Cullum CM. Single neuropsychological test scores associated with rate of cognitive decline in early Alzheimer disease[J]. Clin Neuropsychol, 2014, 28:926-940.
[12] Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J, Freymann J, Jaffe CC, Mikkelsen T, Flanders A. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers:focus on the nonenhancing component of the tumor[J]. Radiology, 2014, 272:484-493.
[13] Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging[J]. Eur Radiol, 2019, 29:4068-4076.
[14] Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J, Feng M. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma[J]. Eur J Radiol, 2019, 121:108647.
[15] Fan Y, Hua M, Mou A, Wu M, Liu X, Bao X, Wang R, Feng M. Preoperative noninvasive radiomics approach predicts tumor consistency in patients with acromegaly:development and multicenter prospective validation[J]. Front Endocrinol (Lausanne), 2019, 10:403.
[16] Fan Y, Jiang S, Hua M, Feng S, Feng M, Wang R. Machine learning-based radiomics predicts radiotherapeutic response in patients with acromegaly[J]. Front Endocrinol (Lausanne), 2019, 10:588.
[17] Zhang Y, Zhang B, Liang F, Liang S, Zhang Y, Yan P, Ma C, Liu A, Guo F, Jiang C. Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types[J]. Eur Radiol, 2019, 29:2157-2165.
[18] Xie H, Ma S, Wang X, Zhang X. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion:preliminary findings and comparison with conventional radiological model[J]. Eur Radiol, 2020, 30:87-98.
[19] Shao Y, Chen Z, Ming S, Ye Q, Shu Z, Gong C, Pang P, Gong X. Predicting the development of normal-appearing white matter with radiomics in the aging brain:a longitudinal clinical study[J]. Front Aging Neurosci, 2018, 10:393.
[20] Cui LB, Liu L, Wang HN, Wang LX, Guo F, Xi YB, Liu TT, Li C, Tian P, Liu K, Wu WJ, Chen YH, Qin W, Yin H. Disease definition for schizophrenia by functional connectivity using radiomics strategy[J]. Schizophr Bull, 2018, 44:1053-1059.
[21] Rahmim A, Huang P, Shenkov N, Fotouhi S, Davoodi-Bojd E, Lu L, Mari Z, Soltanian-Zadeh H, Sossi V. Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images[J]. Neuroimage Clin, 2017, 16:539-544.
[22] Wu ZB. Promote translational medicine research of pituitary adenoma in the era of big data[J]. Zhonghua Yi Xue Za Zhi, 2016, 96:1473-1474.[吴哲褒. 促进大数据时代垂体腺瘤转化医学研究[J]. 中华医学杂志, 2016, 96:1473-1474.]
[23] Reincke M, Sbiera S, Hayakawa A, Theodoropoulou M, Osswald A, Beuschlein F, Meitinger T, Mizuno-Yamasaki E, Kawaguchi K, Saeki Y, Tanaka K, Wieland T, Graf E, Saeger W, Ronchi CL, Allolio B, Buchfelder M, Strom TM, Fassnacht M, Komada M. Mutations in the deubiquitinase gene USP8 cause Cushing's disease[J]. Nat Genet, 2015, 47:31-38.
[24] Karsy M, Guan J, Huang LE. Prognostic role of mitochondrial pyruvate carrier in isocitrate dehydrogenase-mutant glioma[J]. J Neurosurg, 2018, 130:56-66.
[25] Chai Y, Wang C, Liu W, Fan Y, Zhang Y. MPC1 deletion is associated with poor prognosis and temozolomide resistance in glioblastoma[J]. J Neurooncol, 2019, 144:293-301.
[26] Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, Rutten-Jacobs L, Giese AK, van der Laan SW, Gretarsdottir S, Anderson CD, Chong M, Adams HHH, Ago T, Almgren P, Amouyel P, Ay H, Bartz TM, Benavente OR, Bevan S, Boncoraglio GB, Brown RD Jr, Butterworth AS, Carrera C, Carty CL, Chasman DI, Chen WM, Cole JW, Correa A, Cotlarciuc I, Cruchaga C, Danesh J, de Bakker PIW, DeStefano AL, den Hoed M, Duan Q, Engelter ST, Falcone GJ, Gottesman RF, Grewal RP, Gudnason V, Gustafsson S, Haessler J, Harris TB, Hassan A, Havulinna AS, Heckbert SR, Holliday EG, Howard G, Hsu FC, Hyacinth HI, Ikram MA, Ingelsson E, Irvin MR, Jian X, Jiménez-Conde J, Johnson JA, Jukema JW, Kanai M, Keene KL, Kissela BM, Kleindorfer DO, Kooperberg C, Kubo M, Lange LA, Langefeld CD, Langenberg C, Launer LJ, Lee JM, Lemmens R, Leys D, Lewis CM, Lin WY, Lindgren AG, Lorentzen E, Magnusson PK, Maguire J, Manichaikul A, McArdle PF, Meschia JF, Mitchell BD, Mosley TH, Nalls MA, Ninomiya T, O'Donnell MJ, Psaty BM, Pulit SL, Rannikmäe K, Reiner AP, Rexrode KM, Rice K, Rich SS, Ridker PM, Rost NS, Rothwell PM, Rotter JI, Rundek T, Sacco RL, Sakaue S, Sale MM, Salomaa V, Sapkota BR, Schmidt R, Schmidt CO, Schminke U, Sharma P, Slowik A, Sudlow CLM, Tanislav C, Tatlisumak T, Taylor KD, Thijs VNS, Thorleifsson G, Thorsteinsdottir U, Tiedt S, Trompet S, Tzourio C, van Duijn CM, Walters M, Wareham NJ, Wassertheil-Smoller S, Wilson JG, Wiggins KL, Yang Q, Yusuf S, Bis JC, Pastinen T, Ruusalepp A, Schadt EE, Koplev S, Björkegren JLM, Codoni V, Civelek M, Smith NL, Trégouët DA, Christophersen IE, Roselli C, Lubitz SA, Ellinor PT, Tai ES, Kooner JS, Kato N, He J, van der Harst P, Elliott P, Chambers JC, Takeuchi F, Johnson AD, Sanghera DK, Melander O, Jern C, Strbian D, Fernandez-Cadenas I, Longstreth WT Jr, Rolfs A, Hata J, Woo D, Rosand J, Pare G, Hopewell JC, Saleheen D, Stefansson K, Worrall BB, Kittner SJ, Seshadri S, Fornage M, Markus HS, Howson JMM, Kamatani Y, Debette S, Dichgans M; AFGen Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, International Genomics of Blood Pressure (iGEN-BP) Consortium, INVENT Consortium, STARNET, BioBank Japan Cooperative Hospital Group, COMPASS Consortium, EPIC-CVD Consortium, EPIC-InterAct Consortium, International Stroke Genetics Consortium (ISGC), METASTROKE Consortium, Neurology Working Group of the CHARGE Consortium, NINDS Stroke Genetics Network (SiGN), UK Young Lacunar DNA Study, MEGASTROKE Consortium. Multiancestry genome-wide association study of 520, 000 subjects identifies 32 loci associated with stroke and stroke subtypes[J]. Nat Genet, 2018, 50:524-537.
[27] Montaner J, Ramiro L, Simats A, Tiedt S, Makris K, Jickling GC, Debette S, Sanchez JC, Bustamante A. Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke[J]. Nat Rev Neurol, 2020, 16:247-264.
[28] Owolabi M, Peprah E, Xu H, Akinyemi R, Tiwari HK, Irvin MR, Wahab KW, Arnett DK, Ovbiagele B. Advancing stroke genomic research in the age of Trans-Omics big data science:emerging priorities and opportunities[J]. J Neurol Sci, 2017, 382:18-28.
[29] Wahab KW, Tiwari HK, Ovbiagele B, Sarfo F, Akinyemi R, Traylor M, Rotimi C, Markus HS, Owolabi M. Genetic risk of spontaneous intracerebral hemorrhage:systematic review and future directions[J]. J Neurol Sci, 2019, 407:116526.
[30] Sancesario GM, Bernardini S. Alzheimer's disease in the omics era[J]. Clin Biochem, 2018, 59:9-16.