Journal of Internal Medicine Concepts & Practice ›› 2022, Vol. 17 ›› Issue (06): 475-481.doi: 10.16138/j.1673-6087.2022.06.011
• Review article • Previous Articles Next Articles
RUAN Ming1a, HOU Tianzhichao1b,1c, WANG Haiyan2, et al
Received:2022-11-15
Online:2022-12-30
Published:2023-02-27
CLC Number:
RUAN Ming, HOU Tianzhichao, WANG Haiyan, et al. Geometric deep learning and computational medicine research prospects of “preventing disease” in pre diabete[J]. Journal of Internal Medicine Concepts & Practice, 2022, 17(06): 475-481.
| [1] |
Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin[J]. N Engl J Med, 2002, 346(6): 393-403.
doi: 10.1056/NEJMoa012512 URL |
| [2] |
Perreault L, Kahn SE, Christophi CA, et al. Regression from pre-diabetes to normal glucose regulation in the diabetes prevention program[J]. Diabetes Care, 2009, 32(9):1583-1588.
doi: 10.2337/dc09-0523 pmid: 19587364 |
| [3] |
Ramachandran A, Snehalatha C, Mary S, et al. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1)[J]. Diabetologia, 2006, 49(2): 289-297.
doi: 10.1007/s00125-005-0097-z pmid: 16391903 |
| [4] |
Nathan DM, Davidson MB, DeFronzo RA, et al. Impaired fasting glucose and impaired glucose tolerance: implications for care[J]. Diabetes Care, 2007, 30(3): 753-759.
doi: 10.2337/dc07-9920 pmid: 17327355 |
| [5] |
Forouhi NG, Luan J, Hennings S, et al. Incidence of type 2 diabetes in England and its association with baseline impaired fasting glucose: the Ely study 1990-2000[J]. Diabet Med, 2007, 24(2):200-207.
doi: 10.1111/j.1464-5491.2007.02068.x URL |
| [6] |
Gerstein HC, Yusuf S, Bosch J, et al. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose[J]. Lancet, 2006, 368(9541): 1096-1105.
doi: 10.1016/S0140-6736(06)69420-8 pmid: 16997664 |
| [7] |
Knowler WC, Fowler SE, Hamman RF, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study[J]. Lancet, 2009, 374(9702): 1677-1686.
doi: 10.1016/S0140-6736(09)61457-4 pmid: 19878986 |
| [8] |
Song X, Qiu M, Zhang X, et al. Gender-related affecting factors of prediabetes on its 10-year outcome[J]. BMJ Open Diabetes Res Care, 2016, 4(1): e000169.
doi: 10.1136/bmjdrc-2015-000169 URL |
| [9] |
Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study[J]. Lancet, 2008, 371(9626): 1783-1789.
doi: 10.1016/S0140-6736(08)60766-7 URL |
| [10] |
Tuomilehto J, Lindström J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance[J]. N Engl J Med, 2001, 344(18): 1343-1350.
doi: 10.1056/NEJM200105033441801 URL |
| [11] |
Zeng H, Tong R, Tong W, et al. Metabolic biomarkers for prognostic prediction of pre-diabetes: results from a longitudinal cohort study[J]. Sci Rep, 2017, 7(1): 6575.
doi: 10.1038/s41598-017-06309-6 pmid: 28747646 |
| [12] |
Ford ES, Zhao G, Li C. Pre-diabetes and the risk for cardiovascular disease: a systematic review of the evidence[J]. J Am Coll Cardiol, 2010, 55(13): 1310-1317.
doi: 10.1016/j.jacc.2009.10.060 pmid: 20338491 |
| [13] |
Magalhães ME, Cavalcanti BA, Cavalcanti S. Could pre-diabetes be considered a clinical condition? Opinions from an endocrinologist and a cardiologist[J]. Diabetol Metab Syndr, 2010, 2: 2.
doi: 10.1186/1758-5996-2-2 pmid: 20205782 |
| [14] |
Ford ES, Zhao G, Li C. Pre-diabetes and the risk for cardiovascular disease: a systematic review of the evidence[J]. J Am Coll Cardiol, 2010, 55(13): 1310-1317.
doi: 10.1016/j.jacc.2009.10.060 pmid: 20338491 |
| [15] |
Zhang Y, Lee ET, Devereux RB, et al. Prehypertension, diabetes, and cardiovascular disease risk in a population-based sample[J]. Hypertension, 2006, 47(3): 410-414.
doi: 10.1161/01.HYP.0000205119.19804.08 pmid: 16446387 |
| [16] |
Tian J, Sheng CS, Sun W, et al. Effects of high blood pressure on cardiovascular disease events among Chinese adults with different glucose metabolism[J]. Diabetes Care, 2018, 41(9): 1895-1900.
doi: 10.2337/dc18-0918 pmid: 30002198 |
| [17] |
Park JH, Hong JY, Park YS, et al. Association of prediabetes, diabetes, and diabetes duration with biliary tract cancer risk[J]. Metabolism, 2021, 123: 154848.
doi: 10.1016/j.metabol.2021.154848 URL |
| [18] | 缪雅, 刘丽丽, 侯田志超, 等. 糖尿病前期人群恶性肿瘤发病风险分析[J]. 内科学理论与实践, 2022, 17(6): 435-440. |
| [19] | Shen B, Li Y, Sheng CS, et al. Association between age at diabetes onset or diabetes duration and subsequent risk of pancreatic cancer[J]. Lancet Reg Health West Pac, 2022, 30: 100596. |
| [20] | 陈瑜凡, 王燕平, 荣培晶, 等. 糖尿病前期中医证型及证素特点分析[J]. 世界科学技术-中医药现代化, 2022, 24(2): 563-568. |
| [21] | 孙兴利, 谌洪俊, 李鸽, 等. 糖尿病前期人群中医体质调查分析[J]. 贵州中医药大学学报, 2020, 42(5): 96-99. |
| [22] | 李春桂, 曹柏龙, 苗桂珍, 等. 健脾祛湿化痰降浊方为主治疗肥胖型糖尿病前期的临床观察[J]. 陕西中医, 2016, 37(8): 1021-1022. |
| [23] | 吴江雁, 王璇. 中医体质辨识对糖尿病前期的健康干预效果[J]. 新疆中医药, 2019, 37(1): 14-16. |
| [24] | 孙晓娟, 方朝晖. 针灸干预糖尿病前期脾虚痰湿证疗效观察[J]. 中医药临床杂志, 2018, 30(10): 1850-1852. |
| [25] | 王彦军, 焦生林. 穴位埋线治疗2型糖尿病前期患者疗效观察[J]. 上海针灸杂志, 2015, 34(11):1064-1066. |
| [26] | 王金平, 陈燕燕, 巩秋红, 等. 糖尿病和心血管病预防的破冰之旅——大庆糖尿病预防研究30年[J]. 中国科学:生命科学, 2018, 48(8): 902-908. |
| [27] | 李勤, 吴瑞, 王凡, 等. 八段锦对社区糖尿病前期患者干预作用的临床观察[J]. 上海中医药杂志, 2022, 56(5): 49-53. |
| [28] | 张建伟, 吕韶钧, 王继, 等. 太极拳对不同性别2型糖尿病患者治疗效果的比较研究[J]. 北京师范大学学报(自然科学版), 2019, 55(4): 545-550. |
| [29] | Goodfellow I, Bengio Y, Courville A. Deep learning[M]. Cambridge, USA: MIT press, 2016. |
| [30] | LeCun Y, Boser B, Denker J, et al. Handwritten digit recognition with a back-propagation network[J]. Adv Neural Inf Process Syst, 1989, 2: 396-440. |
| [31] |
LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. P IEEE, 1998, 86(11):2278-2324.
doi: 10.1109/5.726791 URL |
| [32] | Bengio Y, LeCun Y. Scaling learning algorithms towards AI[M]// BottouL, ChapelleO, DeCosteD, et al. Large-scale kernel machines, 2007:1-41. |
| [33] |
Hinton GE. To recognize shapes, first learn to generate images[J]. Prog Brain Res, 2007, 165: 535-547.
pmid: 17925269 |
| [34] | Bronstein MM, Bruna J, LeCun Y, et al. Geometric deep learning: going beyond euclidean data[J]. IEEE Signal Process Mag, 2017, 34(4):18-42. |
| [35] |
Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks[J]. IEEE Trans Neural Netw Learn Syst, 2020, 32(1):4-24.
doi: 10.1109/TNNLS.2020.2978386 URL |
| [36] |
Zhou J, Cui GQ, Hu SD, et al. Graph neural networks: a review of methods and applications[J]. AI Open, 2020, 1:57-81.
doi: 10.1016/j.aiopen.2021.01.001 URL |
| [37] | Bronstein MM, Bruna J, Cohen T, et al. Geometric deep learning: grids, groups, graphs, geodesics, and gauges[OL/M]. 2014. https://arxiv.org/pdf/2104.13478.pdf. |
| [38] | Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs[OL/M]. 2013. http://arxiv.org/pdf/1312.6203v3.pdf. |
| [39] | Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data[OL/M]. 2015. https://arxiv.org/pdf/1506.05163.pdf. |
| [40] | Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering[C]. Adv Neural Inf Process Syst, 2016, 29: 3844-3852. |
| [41] | Zheng X, Zhou B, Gao J, et al. How framelets enhance graph neural networks[C]. International Conference on Machine Learning, 2021. |
| [42] | Zheng X, Zhou B, Wang Y, et al. Decimated framelet system on graphs and fast G-framelet transforms[J]. J Mach Learn Res, 2022, 23 (18):1-68. |
| [43] | Zhou B, Jiang Y, Wang Y, et al. Graph neural network for local corruption recovery[EB/C] 2021. https://arxiv.org/pdf/2202.04936v2.pdf. |
| [44] | Zhou B, Liu X, Liu Y, et al. Well-conditioned spectral transforms for dynamic graph representation[C]. Proceedings of the First Learning on Graphs Conference (LoG 2022), 2022. |
| [45] | Jiang X, Zhu R, Li S, et al. Co-embedding of nodes and edges with graph neural networks[J]. IEEE Trans Pattern Anal Mach Intell, 2020.[Epub ahead of print]. |
| [46] | Xu K, Hu W, Leskovec J, et al. How powerful are graph neural networks?[C]. International Conference on Learning Representations, 2019. |
| [47] | Xu K, Li CT, Tian YL, et al. Representation learning on graphs with jumping knowledge networks[C]. International Conference on Machine Learning, 2018. |
| [48] | Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks[C]. International Conference on Learning Representations, 2017. |
| [49] | Gilmer J, Schoenholz SS, Riley PF, et al. Neural message passing for quantum chemistry[C]. International Conference on Machine Learning 2017; 1263-1272. |
| [50] |
Chen S, Eldar YC, Zhao L. Graph unrolling networks: Interpretable neural networks for graph signal denoising[J]. IEEE Trans Signal Process, 2021, 69: 3699-3713.
doi: 10.1109/TSP.2021.3087905 URL |
| [51] | Zhu M, Wang X, Shi C, et al. Interpreting and unifying graph neural networks with an optimization framework[C]. Proceedings of the Web Conference, 2021: 1215-1226. |
| [52] |
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873):583-589.
doi: 10.1038/s41586-021-03819-2 URL |
| [53] | Shen Y, Zhou B, Xiong X, et al. How gaph neural networks enhance convolutional neural networks towards mining the topological structures from histology[C]. ICML Workshop on Computational Biology, 2022. |
| [54] | Zhou B, Lv O, Yi K, et al. Lightweight equivariant graph representation learning for protein engineering[EB/C]. 2022. https://ins.sjtu.edu.cn/people/lhong/papers/articles/LGN-NeurIPS2022-Lightweight%20Equivariant%20Graph %20Representation%20Learning%20for%20Protein%20 Engineering.pdf. |
| [55] | Atz K, Grisoni F, Schneider G. Geometric deep learning on molecular representations[J]. Nat Mac Intell, 2021, 3(12): 1023-1032. |
| [56] | Ganea OE, Huang X, Bunne C, et al. Independent SE(3)-equivariant models for end-to-end rigid protein docking[C]. International Conference on Learning Representations, 2022. |
| [57] | Méndez-Lucio O, Ahmad M, del Rio-Chanona EA, et al. A geometric deep learning approach to predict binding conformations of bioactive molecules[J]. Nat Mac Intell, 2021, 3(12):1033-1039. |
| [58] |
Fang H, Chen L, Knight JC. From genome-wide association studies to rational drug target prioritisation in inflammatory arthritis[J]. Lancet Rheumatol, 2020, 2: e50-e62.
doi: 10.1016/S2665-9913(19)30134-1 URL |
| [59] |
Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic[J]. Cell, 2017, 169(7): 1177-1186.
doi: S0092-8674(17)30629-3 pmid: 28622505 |
| [60] |
De Wolf H, Knezevic B, Burnham KL, et al. A genetics-led approach defines the drug target landscape of 30 immune-related traits[J]. Nat Genet, 2019, 51(7): 1082-1091.
doi: 10.1038/s41588-019-0456-1 pmid: 31253980 |
| [61] |
Fang H. PiER: web-based facilities tailored for genetic target prioritisation harnessing human disease genetics, functional genomics and protein interactions[J]. Nucleic Acids Res, 2022, 50(W1):W583-W592.
doi: 10.1093/nar/gkac379 URL |
| [62] |
Fang H, Xu Y. Priority index: database of genetic targets in immune-mediated disease[J]. Nucleic Acids Res, 2022, 50(D1): D1358-D1367.
doi: 10.1093/nar/gkab994 URL |
| [63] |
Robertson CC, Inshaw JRJ, Onengut-Gumuscu S, et al. Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes[J]. Nat Genet, 2021, 53(7): 962-971.
doi: 10.1038/s41588-021-00880-5 pmid: 34127860 |
| No related articles found! |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||
