대학원 강의

1. 빅데이터 분석:
유전체 정보와 개인라이프로그 정보 활용
서울대 최형진1

2. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

3. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

4. $215MPrecision Medicine Initiative
2015/1/30

6. Integration of Multi-Big-Data

8. 8

9. 2013.4.25. KBS 9시 뉴스

10. 며칠 전 유전자 검사를 받은 40대 남성입니다.
혈액세포의 DNA 상태를 분석해 앞으로 암에 걸릴 위험이 있는지 여부를 판단할 수 있다
고 합니다.
2013.4.25. KBS 9시 뉴스

11. 2013.4.25. KBS 9시 뉴스
60년전 DNA의 구조가 밝혀진 이래 2003년 인간 유전자 지도가 완성됐고, 현재는 어떤 유
전자가 어떤 질병을 일으키는지 분석도 80% 정도 끝난 상태입니다.
예를들어 13번 염색체의 BRCA2 유전자에 이상이 생기면 유방암에 걸릴 확률이 높습니
다. 또 17번 염색체 유전자는 난소암, 7번 염색체 유전자는 비만을 일으킵니다.

12. 1997

13. 13
Heart Disorder 99% Probability
Life Expectancy 30.2 Years

14. 14

15. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

16. Promise of Human Genome Project

17. Tissue Specific Expression
Comprehensive Catalogues of Genomic Data
Variation in the human genome
Mendelian (monogenic) diseases
(N=22,432)
Whole genome sequencing (N=1,000)
Four ethnic groups
(CEU, YRI, JPT, CHB, N=270)
GWAS catalog
Complex (multigenic) traits
(1926 publications and 13410 SNPs)
Disease-related variations
Functional elements
2014-06-29
17

18. All Major Tissues/Organs
All Proteins + All RNAs
2015 Science Tissue-based map of the human proteome
1. Immunohistochemistry (IHC)
24,028 antibodies
(16,975 proteins)
 >13 million IHC images
2. RNA-sequencing
(N=44)
18

19. 111 Reference Human Epigenomes
2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes19

20. 20

21. Data Dimensions
2015.2.19. Nature. Integrative analysis of 111 reference human epigenomes21

22. Network-based Model of
Disease-disease Relationship
2015 Science Uncovering disease-disease relationships through the incomplete interactome22

23. Hypothesis Driven Science Data Driven Science
Hypothesis
Collect
Data
Data
Generate
Hypothesis
Analyze
Analyze

24. Candidate Gene
Approach
Genome-wide
Approach
Choose a Gene
from Prior Knowledge
Analyze the Gene
Analyze ALL Genes
Discover Novel Findings

25. GWAS
(Genome wide association study)
SNP chip
Whole Genome
SNP Profiling (500K~1M SNPs)
Common Variant
Choi HJ, Doctoral Thesis

26. Estrada et al., Nature Genetics, 2012
+ novel targets
for bone biology
Recent largest GWAS
GEFOS consortium

27. 2010 An Environment-Wide
Association Study (EWAS) on Type
2 Diabetes Mellitus
Environment-Wide Association Study (EWAS)
 다양한 환경인자들 

28. GWAS  PheWAS
Phenotype-wide Association Study

29. 1000 개의 질병들
Bioinformatics. 2010 PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.
Phenotype-wide Association Study

30. Genome-Envirome-Phenome-wide
Association Study
Phenome-wide
(Lab,Diagnosis)
Proposal
(Choi)
Genome-wide
Environment-wide
(Life style, diet, exercise, pollution)

31. Anatome-Phenome-wide
Association Study
2015.2.19. Nature. Genetic and epigenetic fine mapping of causal autoimmune disease variants
Phenome
Anatome

32. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

33. 저의 유전자
분석 결과를
반영하여 진료
해주세요!! 헠?
33

34. What is Genomic Medicine?

35. Disease genetic susceptibility
Cancer driver
somatic mutation
Pharmacogenomics
Targeted
Cancer Treatment
(EGFR)
Causal
Variant
Targeted Drug
(MODY-SU)
Drug Efficacy/Side Effect
Related Genotype
(CYP, HLA)
Genetic Diagnosis
(Mendelian,
Cystic fibrosis)
Molecular
Classification
- Prognosis
(Leukemia)
Hereditary
Cancer
(BRCA)
Microbiome
(Bacteria,
Virus)
Genomic Medicine
Risk prediction
(Complex disease,
Diabetes)
Germline Variants
Fetal DNA

36. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

37. Cancer Targeted Therapy
37
Targeted TherapyGenetic TestCancer

38. 38

39. Cancer Rebiopsy
2013 JCO Genomics-Driven Oncology- Framework for an Emerging Paradigm

40. Liquid Biopsy
40

41. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

42. (출처: 금창원 대표님 블로그)
$99
TV CF
42

43. 43

44. 30만원-200만원
44

45. GWAS
N=339,224 individuals
97 BMI-associated loci

46. Tissue Specific Gene Expression
2015 Nature Genetic studies of body mass index yield new insights for obesity biology
- Hypothalamus, Pituitary gland
(appetite regulation)
- Hippocampus, Limbic system
(learning, cognition, emotion,
memory)

47. Gene Set Enrichment Analysis
2015 Nature Genetic studies of body mass index yield new insights for obesity biology

48. Per-allele effect of BMI-associated
loci on body weight
2012 Genetic determinants of common obesity and their value in prediction

49. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic

50. 2015 Nature Genetic studies of
body mass index yield new
insights for obesity biology
Blue: Previous
Red: Novel

51. 2011 Hum Genet. Type 2 diabetes and obesity- genomics and the clinic
TCF7L2

52. ◇◆ ‘parental obesity’
as a test to predict obesity
in adult life
•Dark blue 1–2 yrs
•Green 3–5 yrs
•Red 6–9 yrs
•Light blue 10–14 yrs
•Grey , 15–17 yrs
Genetic Prediction of Obesity Risk
The predictive ability of
the currently
established BMI-
associated loci is poor
2012 Genetic determinants of common obesity and their value in prediction

53. Influence of Genetics on Human Disease
For any condition the overall balance of g
enetic and environmental determinants ca
n be represented by a point somewhere w
ithin the triangle.
53
Single
Locus /
Mendelian
Multiple
Loci or multi-
chromosomal
Environmental
Cystic Fibrosis
Hemophilia A
Examples:
Alzheimer’s Disease
Type II Diabetes
Cardiovascular Disease
Diet
Carcinogens
Infections
Stress
Radiation
Lifestyle
Gene = F8
Gene= CFTR
F8 = Coagulation Factor VIII
CFTR = Cystic Fibrosis Conductance Transmembrane Regulator
Lung Cancer

54. 2008 HMG Genome-based prediction of common diseases- advances and prospects54

55. 2008 HMG Genome-based prediction of common diseases- advances and prospects55

57. Diabetes ≠ Genetic Disease?
• Familial aggregation
– Genetic influences?
– Epigenetic influences
• Intrauterine environment
– Shared family environment?
• Socioeconomic status
• Dietary preferences
• Food availability
• Gut microbiome content
• Overestimated heritability
– Phantom heritability
2012. Drong AW, Lindgren CM, McCarthy MI. Clin Pharmacol Ther. The genetic and epigenetic basis of type 2 diabetes and obesity.
2012. PNAS The mystery of missing heritability- Genetic interactions create phantom heritability57

58. Rare Variant in a Specific Population
• 3756 Latino: whole exome sequencing
 A rare functional variant in candidate gene
 14276: replication  Not found in other ethnic group
2014 JAMA Association of a Low-Frequency Variant in HNF1A With Type 2 Diabetes in a Latino Population

59. 2014 NEJM Null Mutation in Hormone-Sensitive Lipase Gene and Risk of Type 2 Diabetes
Rare Functional Variant
= Monogenic Heritable Disease
All Amish

60. Variants and Disease Susceptibility
2008 NRG Genome-wide association studies for complex traits- consensus, uncertainty and challenges

62. Genotype Based Diabetes Therapy
Diabetes due to KATP channel mutations  sulphonylurea
2007 American Journal of Physiology - Endocrinology and Metabolism. ATP-sensitive K+ channels and disease- from molecule to malady

64. Mendelian (single-gene) genetic
disorder
Known single-gene
candidates testing
Whole Genome or Whole
Exome Sequencing
64

65. Laboratory Director
강현석
65

66. 66

67. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

68. 2012 European Heart Journal. Personalized medicine: hope or hype?
Herceptin
Glivec
68

69. 69
Pharmacogenomic Biomarkers
in Drug Labeling (N=166)
2015.9.14.
Atorvastatin, Azathioprine,
Carbamazepine, Carvediolol,
Clopidogrel, Codein,
Diazepam…..

70. Large Effect Size Variant?
Disease susceptibility variant Pharmacogenetic variant
Environmental
Exposure
Drug
Exposure

72. November 19, 2013

73. November 19, 2013

74. 2013 NEJM A Randomized Trial of
Genotype-Guided Dosing of Warfarin

75. 2013 NEJM A
Randomized Trial of
Genotype-Guided Dosing
of Warfarin
Median
21 days
Median
29 days
Median
44 days
Median
59 days
P<0.001
P=0.003

76. Expected Metformin response
Other drug Metformin usual dose Metformin low dose (S/E)
0% -1% -2%-1.5% -2.5% -3%+0.5%
HbA1c change
Good Response
Genotype
Poor Response
Genotype

77. 77

78. 78

79. 79

80. 80

81. 81

82. Genetics of eating behavior
2011 Genetics of eating behavior

83. Personalized Medicine
Pharmacogenomics
Nutrigenomics
IRS1 SNP GA/AA
High fat/
Low carb
IRS1 SNP GG
Standard
Higher
effect
Similar
effect
2013 Diabetes Care. IRS1 Genotype Modulates Metabolic Syndrome Reversion in Response to 2-Year Weight-Loss Diet Intervention - The POUNDS LOST trial

84. Gene-Environment Interaction
Gene Environment
Disease
Genetic Predisposition Score Sugar-Sweetened Beverages

85. Soda School
No-Soda School
Obese Family Lean Family

86. Genotype Guided Personalized Treatment
Baseline
Genotyping
- Drug metabolism
- DM etiology
- DM complication
1 week 3 month Long term
Genotype based treatment strategy
- Drug choice
- Drug dose
- Lifestyle modification
- Complication evaluation
New
T2DM

87. Pharmacogenetic Tests: 최형진
No
Drug
(N= 10)
Gene
(6 genes=8 bioma
rkers)
Target SNPs
(N=12)
#5
(HJC)
Genotype Interpretation Clinical Interpretation
1 Clopidogrel CYP2C19
rs4244285 (G>A) GG
*1/*1
(EM)
Use standard dosers4986893 (G>A) GG
rs12248560 (C>T) CC
2 Warfarin
VKORC1 rs9923231 (C>T) TT
Low dose
(higher risk of bleeding)
Warfarin dose=0.5~2 mg/day
CYP2C9
rs1799853 (C>T) CC
rs1057910 (A>C) AC
3 Simvastatin SLCO1B1 rs4149056 (T>C) TT Normal
4
Azathioprine (AP),
MP, or TG
TPMT rs1142345 (A>G) AA Normal
5
Carbamazepine
or Phenytoin
HLA-B*1502
rs2844682 (C>T) CT
Normal
rs3909184 (C>G) CC
6 Abacavir HLA-B*5701 rs2395029 (T>G) TT Normal
7 Allopurinol HLA-B*5801 rs9263726 (G>A) GG Normal
Clopidogrel1)
: UM/EM=standard dose, IM/PM= consider alternative antiplatelet agent (eg. prasugrel/ticagrelor)
Warfarin2)
: high dose=5~7 mg/day, medium dose=3~4 mg/day, low dose=0.5~2 mg/day
=0
최형진
+1,000,000
?

88. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

89. 89

91. 2014 NEJM Genotype–Phenotype Correlation — Promiscuity in the Era of Next-Generation Sequencing

92. A Genotype-First Approach to Defining
the Subtypes of a Complex Disease
2014.2.27.

93. Point-of-care
Genotyping
HyBeacon Probes

94. Genome Surgery

96. Future of Genomic Medicine?
Test when neededWithout information Know your type
Blood
type
Geno
type
Here is my
sequence

97. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

98. 98
2015 Sci Transl Med. The emerging field of mobile health

100. 100

101. NFC 혈당측정기
101

102. 데이터베이스 기반 혈당관리
102

103. 103

104. 혈당 변화 실시간 모니터링
저녁 식사전 고혈당
104

105. 구글 헬스 앱 분야 매출 1위 ‘눔(noom)’
105

106. 106

107. 107

108. Date Time name foodType calories unit amount
2014-08-09 0 미역국 0 23 1국그릇 (300ml) 105 g
2014-08-09 0 잡곡밥 0 80 1/4공기 (52.5g) 52 g
2014-08-09 0 열무김치 0 3 1/4소접시 (8.75g) 9 g
2014-08-09 0 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 0 토란대무침 0 28 1/2소접시(46.5g) 46 g
2014-08-09 1 복숭아 0 91 1개 (269g) 268 g
2014-08-09 2 마른오징어 2 88 1/4마리 (25g) 25 g
2014-08-09 2 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 2 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-09 2 복숭아 0 183 2개 (538g) 538 g
2014-08-09 3 복숭아 0 91 1개 (269g) 268 g
2014-08-09 3 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 파프리카 0 6 1/2개 (33.25g) 35 g
2014-08-09 4 식빵 1 92 1장 (33g) 33 g
2014-08-09 4 삶은옥수수 1 197 1개 반 (150g) 150 g
2014-08-09 4 복숭아 0 91 1개 (269g) 268 g
2014-08-09 4 저지방우유 1 72 1컵 (200ml) 180 g
2014-08-10 0 복숭아 0 91 1개 (269g) 268 g
2014-08-10 0 저지방우유 1 36 1/2컵 (100ml) 90 g
2014-08-10 0 두부 0 20 1/4인분 (25g) 25 g
2014-08-10 0 견과류 2 190 1/4 컵 (50g) 31 g
2014-08-10 0 파프리카 0 11 1개 (66.5g) 65 g
108

109. 식사량 실시간 모니터링
0
100
200
300
400
500
600
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
아침
아침간식
점심
점심간식
저녁
저녁간식
2014-08-09 2014-08-10 2014-08-11 2014-08-12 2014-08-13 2014-08-14 2014-08-15
점심 과식
저녁 금식
109

110. 운동량 실시간 모니터링
0
5000
10000
15000
20000
25000
걸음 수
운동X
110

111. 111

112. 운동
식사
112

113. 스마트폰 활용 당뇨병 통합관리
의사
상담 교육
조회/
분석
교육간호사
매주/필요시
진료 처방
2-3달 간격
평가 회의
매주/필요시
식사/
운동
스마트폰
데이터베이스 서버
자가관리
전송
분석
혈당
측정
혈당측정기
113

114. 114

115. 115

116. 피부 사진 원격 진단/처방
116

117. 117

118. 심전도 원격 진단/처방
118

119. 119

120. 120http://startupbank.co.kr/board/board_view.html?ps_boid=75&ps_db=report_s

121. 121 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits
November 3, 2015

122. 122 2015 Cell Metabolism. A Smartphone App Reveals Erratic Diurnal Eating Patterns in Humans that Can Be Modulated for Health Benefits

123. Personalized Nutrition by
Prediction of Glycemic Responses
123
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses
Received: October 5, 2015;
Received in revised form:
October 29, 2015;
Accepted: October 30, 2015;

124. 124
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

125. 125
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

126. 126
2015 Cell. Personalized Nutrition by Prediction of Glycemic Responses

127. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

128. Electronic Health Records
2012 NRG Mining electronic health records- towards better research applications and clinical care
128

129. PCA Analysis혈당
신장기능
129

130. Machine Learning
2014 Big data bioinformatics130

131. Clinical Notes
131

132. 밤동안 저혈당수면 Lt.foot rolling Keep떨림,
식은땀, 현기증, 공복감, 두통, 피로감등의 저혈
당 에 저혈당 이 있을 즉알려주도록 밤사이 특
이호소 수면유지상처와 통증 상처부위 출혈
oozing, severe pain 알리도록 고혈당 처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .당뇨약 이해 잘 하고 수술부위
oozing Rt.foot rolling keep드레싱 상태를 고혈
당 고혈당 의식변화 BST 387 checked.고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 발관리 방법에 .
목표 혈당, 목표 당화혈색소에 .식사를 거르거
나 지연하지 않도록 .식사요법, 운동요법, 약물
요법을 정확히 지키는 것이 중요을 .처방된 당
뇨식이의 중요성과 간식을 자제하도록 .고혈
당 ,,관리 방법 .혈당 정상 범위임rt foot rolling
중으로 pain호소 밤사이 수면양호걱정신경 예
민감정변화 중임감정을 표현하도록 지지하고
경청기분상태 condition 조금 나은 듯 하다고 혈
당 조절과 관련하여 신경쓰는 모습 보이며 혈당
self로 측정하는 모습 보임혈당 조절에 안내하
고 불편감 지속알리도록고혈당 고혈당 의식변
화 고혈당 허약감 지남력 혈당조절 안됨고혈당
으로 인한 구강 내 감염 위해 식후 양치, gargle
등 구강 위생 격려.당뇨환자의 정기점검 내용과
빈도에 .BST 140 으로 저혈당 호소 밤동안 저
혈당수면 Lt.foot rolling Keep떨림, 식은땀, 현
기증, 공복감, 두통, 피로감등의 저혈당 에 저
혈당 이 있을 즉알려주도록 pain 및 불편감 호
소 WA 잘고혈당 고혈당 의식변화 고혈당 허
약감 지남력 혈당조절 안됨식사요법, 운동요법,
약물요법을 정확히 지키는 것이 중요을 .저혈당
/고혈당 과 대처법에 .혈당정상화, 표준체중의
유지, 정상 혈중지질의 유지에 .고혈당 ,,관리
방법 .혈당측정법,인슐린 자가 투여법, 경구투
약,수분 섭취량,대체 탄수화물,의료진의 도움이
필요한 사항에 교혈당 정상 범위임수술부위
oozing Rt.foot rolling keep수술 부위 (출혈, 통
증, 부종)수술부위 출혈 상처부위 oozing
Wound 당겨지지 않도록 적절한 체위 취하기
설명감염 발생 위험 요인 수술부위 출혈 밤동안
간호기록지 Word Cloud
Natural Language Processing (NLP)132

133. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

134. 134

135. 135

136. 136

137. 137

138. Korean Society for
Bone and Mineral
Research
Anti-hypertensive
prescriptions
(2008-2011)
N = 8,315,709
New users
N = 2,357,908
Age ≥ 50 yrs
Monotherapy
Compliant user (MPR≥80%)
No previous fracture
N = 528,522
Prevalent users
N = 5,957,801
Excluded
Age <50
Combination therapy
Inadequate compliance
Previous fracture
N = 1,829,386
Final study population
심평원 빅데이터 연구
고혈압약과 골절
Choi et al., 2015 International Journal of Cardiology138

139. Compare Fracture Risk
Comparator?Hypertension
CCB
High
Blood Pressure
Fracture
Risk
BB
Non-
user
Healthy
Non-
user
Cohort study (Health Insurance Review & Assessment Service)
New-user design (drug-related toxicity)
Non-user comparator (hypertension without medication)
2007 20112008
Choi et al., 2015 International Journal of Cardiology139

140. 140

141. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

142. 2013 Science Structural and Functional Brain Networks- From Connections to Cognition

143. Green Lines: orexigenic pathways
Red lines: dopaminergic pathways
2013 The contribution of brain reward circuits to the obesity epidemic
2014 Obesity – A neuropsychological disease- Systematic review and neuropsychological mode
A, amygdala;
H, hypothalamus;
NA, nucleus accumbens,
PFC, prefrontal cortex;
VTA, ventral tegmental area
143
tDCS Neuromodulation Controls
Feeding Behavior via
Food Reward Activity and Connectivity
Neuromodulation Brain Activity Feeding Behavior
Brain Connectivity

145. fMRI analysis
2013 Science Functional interactions as big data in the human brain

146. 2013 Science Functional interactions as big data in the human brain

147. 147
2013 Science Functional interactions as big data in the human brain
2012 Decoding subject-driven cognitive states with whole-brain connectivity patterns

148. 2014
Radiomics
2014 Nature Communications. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

149. Quantitative nuclear morphometry
2015 Laboratory Investigation. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis
of whole slide images

150. Contents
1. Introduction
2. Human Genome Project and Beyond
3. Genome Data
① Cancer Targeted Therapy
② Disease Risk (Common, Rare)
③ Pharmacogenomics
④ Others (Fetal DNA, Microbiome)
4. Sensor/Mobile Data
5. Electrical Health Records
6. National Healthcare Data
7. Medical Images
8. Biomedical Big Data + Artificial Intelligence

151. 151

152. 152

153. Multidimensional Architecture
153
Gene
Hormone
Psychology
Behavior
Phenotype
Outcome
Metabolic
Disease
Vascular
Disease
Environment

154. Healthcare Big Data
+ Artificial Intelligence
154
Healthcare Big Data Machine Learning
Novel Insights and Applications

155. 155

157. 157
In a scan of 3,000 images, IBM
technology was able to spot
melanoma with an accuracy
of about 95 percent, much
better than the 75 percent to
84 percent average of today's
largely manual methods.
IBM Research will continue to
work with Sloan Kettering to
develop additional
measurements and
approaches to further refine
diagnosis, as well as refine
their approach through larger
sets of data.
Dec 17, 2014

158. 158
Aug. 11, 2015
IBM is betting that the same technology that
recognizes cats can identify tumors and other signs of
diseases.
In the long run, IBM and others in the field hope such
systems can become reliable advisers to
radiologists, dermatologists and other practitioners
who analyze images—especially in parts of the world
where health-care providers are scarce.
While IBM hopes Watson will learn to interpret
Merge’s images, it also expects the combination of
imagery, medical records and other data to reveal
patterns relevant to diagnosis and treatment that a
human physician may miss, ushering in an era of
computer-assisted care. Two other recent IBM
acquisitions, Phytel Inc. and Explorys Inc., yielded 50
million electronic medical records.

159. 159

160. 160
Environment
Gene
Eat
Exercise
Metabolism
Brain
Glucose
DM
Blood Pressure
HTN
Cardiovascular
Disease
Cognitive
Hormone
Behavior
Psychotherapy
Behavior Therapy
Policy Making
Genetic Testing
Neuroimaging
Neuromodulation
Drug
Drug
Lab
Survey
Survey
Sensor
DrugDiagnosis
EMR
Government
Data
Mining

161. Environment Survey
NeuroimagingGenetics Lab/Hormone Hospital
Cognitive
Personalize
Psychotherapy
Dietary
Intervention
Exercise
Intervention
Food
Exercise
Glucose
Mobile
Drug
Neuromodulation
Monitoring