1) Leung MK, et al : Proc. IEEE, 104 : 176-197, 2016
2) Angermueller C, et al : Molecular Syst Biol, 12 : 878, 2016
3) Quang D, et al : Bioinformatics, 31 : 761-763, 2014
4) Gawehn E, et al : Molecular Informat, 35 : 3-14, 2016
5) Fakoor R, et al : "Using deep learning to enhance cancer diagnosis and classification" in Proc Int Conf Mach Learn, pp1-7, 2013
6) Ibrahim R, et al : Conf Proc IEEE Eng Med Biol Soc : 3957-3960, 2014
7) Khademi M & Nedialkov NS : "Probabilistic graphical models and deep belief networks for prognosis of breast cancer" in Proc IEEE 14th Int Conf Mach Learn Appl, pp727-732, 2015
8) Pesapane F, et al : Eur Radiol Exp, 2 : 35, 2018
9) Topol EJ : Nat Med, 25 : 44-56, 2019
10) Titano JJ, et al : Nat Med, 24 : 1337-1341, 2018
11) Arbabshirani MR, et al : NPJ Digit Med, 1 : 9, 2018
12) Chilamkurthy S, et al : Lancet, 392 : 2388-2396, 2018
13) Nam JG, et al : Radiology, 2018 (https://doi.org/10.1148/radiol.2018180237)
14) Singh R, et al : PLoS ONE, 13 : e0204155, 2018
15) Lehman CD, et al : Radiology, 2018 (http://doi.org/10.1148/radiol.2018180694)
16) Lindsey R, et al : Proc Natl Acad Sci USA, 115 : 11591-11596, 2018
17) Ehteshami Bejnordi B, et al : JAMA, 318 : 2199-2210, 2017
18) Coudray N, et al : Nat Med, 24 : 1559-1567, 2018
19) Capper D, et al : Nature, 555 : 469-474, 2018
20) Steiner DF, et al : Am J Surg Pathol, 42 : 1636-1646, 2018
21) Liu Y, et al : APLM, 2018 (https://doi.org/10.5858/arpa.2018-0147-OA)
22) Esteva A, et al : Nature, 542 : 115-118, 2017
23) Haenssle HA, et al : Ann Oncol, 29 : 1836-1842, 2018
24) Han SS, et al : J Invest Dermatol, 138 : 1529-1538, 2018
25) Gulshan V, et al : JAMA, 316 : 2402-2410, 2016
26) Abramoff M, et al : NPJ Digit Med, 1 : 39, 2018
27) Kanagasingam Y, et al : JAMA Netw Open, 1 : e182665, 2018
28) Long E, et al : Nat Biomed Eng, 1 : 1-8, 2017
29) De Fauw J, et al : Nat Med, 24 : 1342-1350, 2018
30) Burlina PM, et al : JAMA Ophthalmol, 135 : 1170-1176, 2017
31) Brown JM, et al : JAMA Ophthalmol, 136 : 803-810, 2018
32) Kermany DS, et al : Cell, 172 : 1122-1131, 2018
33) Mori Y, et al : Ann Intern Med, 169 : 357-366, 2018
34) Wang P, et al : Nat Biomed Eng, 2 : 741-748, 2018
35) Madani A, et al : NPJ Digit Med, 1 : Article number 6, 2018
36) Zhang J, et al : Circulation, 138 : 1623-1635, 2018
37) Esteva A, et al : Nature, 542 : 115-118, 2017
38) Nie D, et al : "3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients" in Proc MICCAI, pp212-220, 2016
39) Ruder S : Transfer Learning - Machine Learning's Next Frontier : (http://ruder.io/transfer-learning/)
40) Shin HC, et al : IEEE Trans Med Imag, 35 : 1285-1298, 2016
41) Chen H, et al : IEEE J Biomed. Health Inform, 19 : 1627-1636, 2015
42) Greenspan H, et al : IEEE Trans Med Imag, 35 : 1153-1159, 2016
43) Cheng JZ, et al : Sci Rep, 6 : 24454, 2016
44) Shan J & Li L : "A deep learning method for microaneurysm detection in fundus images" in Proc IEEE Connected Health, Syst Eng Technol, pp357-358, 2016
45) Hu C, et al : "Clinical decision support for alzheimer's disease based on deep learning and brain network" in Proc Int Conf Commun, pp1-6, 2016
46) Mansoor A, et al : IEEE Trans Med Imag, 35 : 1856-1865, 2016
47) Pouladzadeh P, et al : "Food calorie measurement using deep learning neural network" in Proc IEEE Int Instrum Meas Technol Conf Proc, pp1-6, 2016
48) Sun L, et al : "DL-SFA : Deeply-learned slow feature analysis for action recognition" in Proc IEEE Conf Comput Vis Pattern Recognit, pp2625-2632, 2014
49) Yalcin H : Human activity recognition using deep belief networks" in Proc Signal Process Commun Appl Conf, pp1649-1652, 2016
50) Zeng M, et al : "Convolutional neural networks for human activity recognition using mobile sensors" in Proc MobiCASE, pp197-205, 2014
51) Poggi M & Mattoccia S : "A wearable mobility aid for the visually impaired based on embedded 3d vision and deep learning" in Proc IEEE Symp Comput Commun, pp208-213, 2016
52) Huang J, et al : "Sign language recognition using sreal-sense," in Proc IEEE ChinaSIP, pp166-170, 2015
53) Yan Y, et al : "A restricted Boltzmann machine based two-lead electrocardiography classification" in Proc 12th Int Conf Wearable Implantable Body Sens Netw, pp1-9, 2015
54) Shin H, et al : "Interleaved text/image deep mining on a large-scale radiology database for automated image interpretation" CoRR, vol.abs/1505.00670, 2015
55) Liang Z, et al : "Deep learning for healthcare decision making with emrs" in Proc Int Conf Bioinformat Biomeds, pp556-559, 2014
56) Nie L, et al : IEEE Trans Knowl Data Eng, 27 : 2107-2119, 2015
57) Miotto R, et al : Sci Rep, 6 : 1-10, 2016
58) Futoma J, et al : J Biomed Informat, 56 : 229-238, 2015
59) Lipton ZC, et al : "Learning to diagnose with LSTM recurrent neural networks" CoRR, vol.abs/1511.03677, 2015
60) Mehrabi S, et al : "Temporal pattern and association discovery of diagnosis codes using deep learning," in Proc In Conf Healthcare Informat, pp408-416, 2015
61) Che Z, et al : "Distilling knowledge from deep networks with applications to healthcare domain," ArXiv e-prints, 2015
62) Huang T, et al : Big Data Res, 2 : 2-11, 2015
63) Ong BT, et al : Neural Comput, 27 : 1-14, 2015
64) Zhao L, et al : "Simnest : Social media nested epidemic simulation via online semisupervised deep learning" in Proc IEEE Int Conf Data Mining, pp639-648, 2015
65) Kendra RL, et al : J Med Internet Res, 17 : e154, 2015
66) Garimella VRK, et al : "Social media image analysis for public health" in Proc CHIConf Human Factors Comput Syst, pp5543-?5547, 2016
67) Phan N, et al : "Social restricted Boltzmann machine : Human behavior prediction in health social networks"in Proc. IEEE/ACM Int Conf Adv Social Netw Anal Mining, pp424-431, 2015
68) Horvitz E & Mulligan D : Science, 349 : 253-255, 2015
69) Erhan D, et al : "Visualizing higherlayer features of a deep network"Univ Montreal, Montreal, QC, Canada, Tech Rep, 1341, 2009
70) Szegedy C, et al : "Intriguing properties of neural networks" CoRR, vol.abs/1312.6199, 2013
71) Scannell JW, et al : Nat Rev Drug Discov, 11 : 191-200, 2012
72) Kaggle team, Deep Learning How I Did It : Merck 1st place interview (http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/)
73) Dahl GE, et al : arXiv : 1406.1231, 2014
74) Hase T, Tanaka H, et al : Artificial intelligence based computational framework for drugtarget prioritization and inference of novel repositionable drugs for Alzheimer' disease, bioRxiv (https://www.biorxiv.org/) : 2020
75) Wang D, et al : Structural Deep Network Embedding, KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 1225-1234, 2016
76) HPRD-Human Protein Reference Database (https://www.hsls.pitt.edu/obrc/index.php?page=URL1055173331)
77) Chen T, et al : XGBoost : arXiv : 1603.02754v3, 2016
78) Chaw NV, et al : Journal of Artificial Intelligence Research, 16 : 321-357, 2002
79) Bustos FJ, et al : Brain, 140 : 3252-3268, 2017
80) Wang L, et al : Proc Natl Acad Sci USA, 109 : 16743-16748, 2012
81) Wang PL, et al : Neurosci Res, 87 : 2105-2114, 2009
82) "The Hologenome Concept : Human, Animal and Plant Microbiota" (Rosenberg E, et al), Springer, 2013
83) Chen R, et al : Cell, 148 : 1293-1307, 2012
84) mobi health news : FDA clears WellDoc's non-RX version of BlueStar, its mobile diabetes management tool : 2017 (https://www.mobihealthnews.com/content/fda-clears-welldocs-non-rx-version-bluestar-its-mobile-diabetes-management-tool)