Supervised Clustering Based on DPClusO: Prediction of Plant-Disease Relations Using Jamu Formulas of KNApSAcK Database

Hindawi Publishing Corporation
BioMed Research International
Volume 2014, Article ID 831751, 15 pages
http://dx.doi.org/10.1155/2014/831751

Research Article
Supervised Clustering Based on DPClusO:
Prediction of Plant-Disease Relations Using Jamu
Formulas of KNApSAcK Database
Sony Hartono Wijaya,1,2 Husnawati Husnawati,3 Farit Mochamad Afendi,4
Irmanida Batubara,5 Latifah K. Darusman,5 Md. Altaf-Ul-Amin,1 Tetsuo Sato,1
Naoaki Ono,1 Tadao Sugiura,1 and Shigehiko Kanaya1
1

Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
Department of Computer Science, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia
3
Department of Biochemistry, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia
4
Department of Statistics, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia
5

Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3,
Bogor 16151, Indonesia

2

Correspondence should be addressed to Shigehiko Kanaya; skanaya@gtc.naist.jp
Received 30 November 2013; Accepted 18 February 2014; Published 7 April 2014
Academic Editor: Samuel Kuria Kiboi
Copyright © 2014 Sony Hartono Wijaya et al. his is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Indonesia has the largest medicinal plant species in the world and these plants are used as Jamu medicines. Jamu medicines are
popular traditional medicines from Indonesia and we need to systemize the formulation of Jamu and develop basic scientiic
principles of Jamu to meet the requirement of Indonesian Healthcare System. We propose a new approach to predict the relation
between plant and disease using network analysis and supervised clustering. At the preliminary step, we assigned 3138 Jamu
formulas to 116 diseases of International Classiication of Diseases (ver. 10) which belong to 18 classes of disease from National
Center for Biotechnology Information. he correlation measures between Jamu pairs were determined based on their ingredient
similarity. Networks are constructed and analyzed by selecting highly correlated Jamu pairs. Clusters were then generated by using
the network clustering algorithm DPClusO. By using matching score of a cluster, the dominant disease and high frequency plant
associated to the cluster are determined. he plant to disease relations predicted by our method were evaluated in the context of

previously published results and were found to produce around 90% successful predictions.

1. Introduction
Big data biology, which is a discipline of data-intensive
science, has emerged because of the rapid increasing of
data in omics ields such as genomics, transcriptomics,
proteomics, and metabolomics as well as in several other
ields such as ethnomedicinal survey. he number of medicinal plants is estimated to be 40,000 to 70,000 around the
world [1] and many countries utilize these plants as blended
herbal medicines, for example, China (traditional Chinese
medicine), Japan (Kampo medicine), India (Ayurveda, Siddha, and Unani), and Indonesia (Jamu). Nowadays, the use

of traditional medicines is rapidly increasing [2, 3]. hese
medicines consist of ingredients made from plants, animals,
minerals, or combination of them. he traditional medicines
have been used for generations for treatments of diseases
or maintaining health of people and the most popular form
of traditional medicine is herbal medicine. Blended herbal
medicines as well as single herb medicines include a large
number of constituent substances which exert efects on

human physiology through a variety of biological pathways.
he KNApSAcK Family database systems can be used to
comprehensively understand the medicinal usage of plants
based upon traditional and modern knowledge [4, 5]. his

2

BioMed Research International

Table 1: List of diseases using International Classiication of Diseases ver. 10 (class of disease IDs correspond to Table 2).
ID

Disease

Class of
disease

1
2
3

4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43

44
45

Abdominal pain
Abdominal pain, diarrhea
Acne
Acne, skin problems (cosmetics)
Amenorrhoea, dysmenorrhea
Amenorrhoea, irregular menstruation
Anaemia
Appendicitis, urinary tract infection, tonsillitis
Arthralgia
Arthralgia, arthritis
Asthma
Benign prostatic hyperplasia (Bph)
Breast disorder
Bromhidrosis
Bronchitis
Cancer
Cancer pain

Cancer, inlammation
Colic abdomen, bloating (in infant)
Common cold
Common cold, dyspepsia, insect bites
Common cold, inluenza
Cough
Degenerative disease
Dermatitis, urticaria, erythema
Diabetes
Diabetic gangrene
Diarrhea
Diarrhea, abdominal pain
Diseases of the eye
Disorders in pregnancy
Dysmenorrhea
Dysmenorrhea, irregular menstruation
Dysmenorrhea, menstrual syndrome
Dyspepsia
Dyspnoea
Dyspnoea, cough, orthopnoea

Fatigue
Fatigue, anaemia, loss appetite
Fatigue, lack of sexual function
Fatigue, low back pain
Fatigue, myalgia, arthralgia
Fatigue, osteoarthritis
Fertility problem
Fever

3
3
16
16
6
6
1
3
11
11
15

10
6
16
15
2
2
2
3
15
15, 3, 16
15
15
14
16
14
16
3
3
5
6

6
6
6
3
15
15
11
1
6
11
11
11
6, 10
0

Table 1: Continued.
ID

Disease

46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

Gastritis, gastric ulcer
Haemorrhoids
Headache
Heart diseases
Heartburn
Hepatitis, other diseases of liver
Hypercholesterolaemia
Hypertension
Hypertension, diabetes
Hypertension, hypercholesterolaemia
Hyperuricemia
Immunodeiciency
Indigestion (K.30)
Indigestion, lose appetite
Infertility
Irregular menstruation, menstruation
syndrome
Kidney diseases
Lactation problems
Leukorrhoea (Vaginalis)
Leukorrhoea (Vaginalis), dysmenorrhoea
Lose appetite
Lose appetite, underweight
Low back pain, myalgia, arthralgia
Low back pain, myalgia, constipation
Low back pain, urinary tract infection
Lung diseases
Malaise and Fatigue
Malaise and Fatigue, Constipation
Malaise and Fatigue, Fertility Problems
Malaise and Fatigue, Low Back Pain
Malaise and Fatigue, Sexual Dysfunction
Malaise and Fatigue, Skin Problems
(Cosmetics)
Malaria, anaemia
Meno-metrorrhagia
Menopausal syndrome
Menopause/menstrual syndrome, leukorrhoea
(vaginalis)
Menstrual syndrome
Menstrual syndrome, fatigue
Migraine
Mood disorder
Myalgia, arthralgia
Nausea/vomiting of pregnancy
Osteoarthritis
Osteoarthritis, fatigue

61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89

Class of
disease
3
1
13
8
3, 8
3
14
8
14
14
1
9
3
3
6, 10
6
17
6
6
6
3
14
11
11
17
15
11
11
10, 11
11
11, 6, 10
16
1
6
6
6
6
6
13
18
11
6
11
11

BioMed Research International

3

Table 1: Continued.
ID
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106

Disease

Overweight, obesity
Paralysis
Post partum syndrome
Prevent from overweight
Respiratory infection due to smoking
Respiratory tract infection
Rheumatoid arthritis, gout
Secondary amenorrhea
Secondary amenorrhea, irregular menstruation
Sexual dysfunction, fatigue
Skin diseases
Skin problems (cosmetics)
Sleeping and Mood Disorders
Sleeping disorders
Stomatitis
Stomatitis, gingivitis, tonsilitis
Stone in kidney (N20.0)
107 Stone in kidney (N20.0), urinary bladder stone
(N21.0)
108 Tonsilitis
109 Tonsilofaringitis
110 Toothache
111 Typhoid, dyspepsia
112 Ulcer of anus and rectum
113 Underweight, lose appetite
114 Urinary tract infection (urethritis)
115 Vaginal discharges
116 Vaginal diseases

Class of
disease
14
13
6
14
15
15
11
6
6
6, 10
16
16
18
18
3
3
17
17
4
4
13
3
3
3
17
6
6

database has information about the selected herbal ingredients, that is, the formulas of Kampo and Jamu, omics
information of plants and humans, and physiological activities in humans. Jamu is generally composed based on the
experience of the users for decades or even hundreds of
years. However, versatile scientiic analyses are needed to
support their eicacy and their safety. Attaining this objective
is in accordance with the 2010 policy of the Ministry of
Health of Indonesian Government about scientiication of
Jamu. hus, it is required to systemize the formulations
and develop basic scientiic principles of Jamu to meet the
requirement of Indonesian Healthcare System. Afendi et al.
initiated and conducted scientiic analysis of Jamu for inding
the correlation between plants, Jamu, and their eicacy using
statistical methods [6–8]. hey used Biplot, partial least
squares (PLS), and bootstrapping methods to summarize the
data and also focused on prediction of Jamu formulations.
hese methods give a good understanding about relationship
between plants, Jamu, and their eicacy. Among 465 plants
used in 3138 Jamu, 190 plants were shown to be efective
for at least one eicacy and these plants were considered

to be the main ingredients of Jamu. he other 275 plants
are considered to be supporting ingredients in Jamu because
their eicacy has not been established yet.
Network biology can be deined as the study of the
network representations of molecular interactions, both to
analyze such networks and to use them as a tool to make
biological predictions [9]. his study includes modelling,
analysis, and visualizations, which holds important task in
life science today [10]. Network analysis has been increasingly
utilized in interpreting high throughput data on omics information, including transcriptional regulatory networks [11],
coexpression networks [12], and protein-protein interactions
[13]. We can easily describe relationship between entities in
the network and also concentrate on part of the network
consisting of important nodes or edges. hese advantages can
be adopted for analyzing medicinal usage of plants in Jamu
and diseases. Network analysis provides information about
groups of Jamu that are closely related to each other in terms
of ingredient similarity and thus allows precise investigation
to relate plants to diseases. On the other hand, multivariate
statistical methods such as PLS can assign plants to eicacy by
global linear modeling of the Jamu ingredients and eicacy.
However, there is still lack of appropriate network based
methods to learn how and why many plants are grouped in
certain Jamu formula and the combination rule embedding
numerous Jamu formulas.
It is needed to explore the relationship between Indonesian herbal plants used in Jamu medicines and the diseases
which are treated using Jamu medicines. When efectiveness
of a plant against a disease is irmly established, then further
analysis about that plant can be proceeded to molecular level
to pinpoint the drug targets. he present study developed
a network based approach for prediction of plant-disease
relations. We utilized the Jamu data from the KNApSAcK
database. A Jamu network was constructed based on the
similarity of their ingredients and then Jamu clusters were
generated using the network clustering algorithm DPClusO
[14, 15]. Plant-disease relations were then predicted by determining the dominant diseases and plants associated with
selected Jamu clusters.

2. Methods
2.1. Concept of the Methodology. Jamu medicines consist
of combination of medicinal plants and are used to treat
versatile diseases. In this work we exploit the ingredient
similarity between Jamu medicines to predict plant-disease
relations. he concept of the proposed method is depicted
in Figure 1. In step 1 a network is constructed where a node
is a Jamu medicine and an edge represents high ingredient
similarity between the corresponding Jamu pair. In Figure 1,
the nodes of the same color indicate the Jamu medicines used
for the same disease. he similarity is represented by Pearson
correlation coeicient [16, 17]; that is,
corr (�, �) =

∑��=1 (�� − �) (�� − �)

√∑� (�� − �)2 ∑� (�� − �)2
�=1
�=1

,

(1)

4

BioMed Research International

Table 2: Distribution of Jamu formulas according to 18 classes of disease (classes of diseases are determined by NCBI in ID1 to ID16 and by
the present study in ID17 and ID18 represented by asterisks in Ref. columns).
ID

Class of disease (NCBI)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Blood and lymph diseases
Cancers
he digestive system
Ear, nose, and throat
Diseases of the eye
Female-speciic diseases
Glands and hormones
he heart and blood vessels
Diseases of the immune system
Male-speciic diseases
Muscle and bone
Neonatal diseases
he nervous system
Nutritional and metabolic diseases
Respiratory diseases
Skin and connective tissue
he urinary system
Mental and behavioral disorders

Ref.

Number of Jamu

Percentage

NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
NCBI
∗
∗

201
32
457
2
1
382
0
57
22
17
649
0
32
576
313
163
90
21

6.41
1.02
14.56
0.06
0.03
12.17
—
1.82
0.70
0.54
20.68
—
1.02
18.36
9.97
5.19
2.87
0.67

he number of Jamu classiied into multiple disease classes
he number of Jamu unclassiied

119
4

3.79
0.13

Total Jamu formulas

3138

100.00

where �� is the weight of plant-� in Jamu �, �� is the weight
of plant-� in Jamu �, � is mean of Jamu �, and � is mean
of Jamu �. he higher similarity between Jamu pairs the
higher the correlation value. In the present study, �� and
�� are assigned as 1 or 0 in cases the �th plant is, respectively, included or not included in the formula. Under such
condition, Pearson correlation corresponds to fourfold point
correlation coeicient; that is,
corr (�, �) =

�� − ��
,
√(� + �) (� + �) (� + �) (� + �)

(2)

where �, �, �, and � represent the numbers of plants included
in both � and �, in only �, in only �, and in neither � nor
�, respectively.
In step 2 the Jamu clusters are generated using network clustering algorithm DPClusO. DPClusO can generate
clusters characterized by high density and identiied by
periphery; that is, the Jamu medicines belonging to a cluster
are highly cohesive and separated by a natural boundary. Such
clusters contain potential information about plant-disease
relations.
In step 3 we assess disease-dominant clusters based on
matching score represented by the following equation:
matching score
=

number of Jamu belonging to the same disease
.
total number of Jamu in the cluster
(3)

Matching score of a cluster is the ratio of the highest number
of Jamu associated with a single disease to the total number
of Jamu in the cluster. We assign a disease to a cluster for
which the matching score is greater than a threshold value.
In step 4, we determine the frequency of plants associated
with a cluster if and only if a disease is assigned to it in the
previous step. he highest frequency plant associated to a
cluster is considered to be related to the disease assigned to
that cluster. True positive rates (TPR) or sensitivity was used
to evaluate resulting plants. TPR is the proportion of the true
positive predictions out of all the true predictions, deined by
the following formula [18]:
TPR =

TP
,
TP + FN

(4)

where true positive (TP) is the number of correctly classiied
and false negative (FN) is the number of incorrectly rejected
entities. We refer to the proposed method as supervised
clustering because ater generation of the clusters we narrow
down the candidate clusters for further analysis based on
supervised learning and thus improve the accuracy of prediction of the proposed method.

3. Result and Discussion
3.1. Construction and Comparison of Jamu and Random
Networks. We used the same number of Jamu formulas from
previous research [6], 3138 Jamu formulas, and the set union

BioMed Research International

5

Input: Jamu formulas

Step 1
Constructing ingredient correlation network

Step 2
Extracting highly connected Jamu

A

B

C

D

Step 3
Supervised analysis for voting utilization

A

Step 4
Listing ingredients

B

C

D

Output: plant-disease relations

Figure 1: Concept of the methodology: network construction based on ingredient similarity between individual Jamu medicines, network
clustering, and classiication of medicinal plants to dominant disease.

J00550

J01529 J02944
J03080

J00027
J01651
J00468
J02515

J02350
J00441
J02682

J02315
J01838

J01934

J02557

J01379
J01378
J02919

J02465
J00540
J01177
J00879
J01645
J02933

J02661
J01212

J02351

J02344

J03124

J02310
J00455
J02300

J00844

J00400J02364

J02341

J03042

J00547
J01315

J01948

J01179
J02782
J00326
J00450
J01030

J00497
J01686
J02092

J00512 J01643

J00840
J02595
J01441 J02813

J02604

J01301

J00854
J01705
J00868

J00980

J02256

J01325

J01813

J00933

J00893

J00181

J01854

J00310
J02513
J02606
J02591
J03011
J01364
J00268
J01100 J01370
J01470
J02598
J01488
J03129
J00466
J00284J00924
J02599

J02512 J01308
J02628

J02343

J01424

J02941

J00258

J00250

J01181

J01927

J03035

J02963

J02586
J00411 J00183
J03071 J01575

J02408

J01123
J01578

J02187

J02185

J00953

J02758

J02103

J03004
J01359

J01090

J02886

J02085
J02990
J02316

J02993

J02991

J02983

J01571

J02083

J02305

J02945

J00652

J01910

J02838

J01188

J01180

J01087

J00123

J02852

J00154
J00156J00155

J01824
J02851
J02845

J02850
J00797
J02808

J00813
J00815J01367
J00085

J02875

J02908

J02861

J00612

J00711

J02876
J01234

J02923
J00640
J01780

J02885 J00614
J03087
J00633
J00646 J02929

J00337

J00086
J00075
J00355
J00818
J02854

J02874

J00635
J02771

J02084

J00592
J02868J01279

J01781

J02849
J02844
J02839

J00233
J00120
J02853
J00148
J01393
J01702
J02791
J00230
J01861
J01126
J02802
J01579
J00733
J03082
J01835J02801
J02856J01431
J01354
J01528
J01498
J01825
J02666
J00713J02036 J02840
J00898
J01615
J01909
J02670
J00153
J00158
J02859
J00332
J02831
J02261J02969
J02022
J01845
J01565
J02833
J01863
J01680
J02922
J02400
J01394
J02035
J00306
J00891
J01551
J02893 J01832
J00958
J00149
J02884
J01395
J02387
J02793 J02656
J00427
J01983
J03088
J02135
J00068J02374
J00226
J00986
J02203
J00894
J02262
J02263
J02803
J01704
J01617
J01500
J00160
J03099
J00088
J00054
J00821
J02792
J01569
J01690
J01502J02655
J00307
J00892
J01361
J02847
J02789J02754
J01698
J02032
J01649J02137
J02123
J03081
J02855 J02317
J01700
J00071
J02029 J02156
J03100
J02751
J02373
J00147J02038
J01432
J02045
J03097J00758
J01064
J02662
J01616
J02663
J00286
J02812 J02159 J01862
J02326
J02653
J02126
J01703
J01053
J00227
J02260
J02342
J03098
J02950
J01614
J02138
J02136
J01648
J02153
J00102
J02033
J02028
J02075
J01501
J02848
J01550 J01681
J03085
J01697
J02811
J02202
J01831J02925
J02995
J00985
J02399
J01827
J02339
J02124
J02152 J02830J01826
J01836
J01613
J00798
J02497
J01699
J00121
J01908
J02787
J02788
J02660 J02259
J00150
J02568
J01618
J00331
J01377 J02402
J01580
J01434
J03115
J01270
J01688
J02658
J02753 J02659
J00087
J02204
J01837
J02388
J01609
J02530
J02879
J03101
J00073
J00897J01063
J00157 J01689
J00067
J01855
J01120
J02125
J03084
J03086
J00734
J02354
J01576
J02809
J03095
J01353
J03083
J01368
J02157
J02258
J02948
J02039
J02786
J01336
J02037
J00735
J01548
J01564
J01683
J01685
J01830 J02034
J02469
J00146
J01122
J02810
J00161
J03094
J02949
J00053
J00066
J00820
J02968
J01271
J00077
J01583
J02031
J01527
J01121
J02529
J02401
J01360

J01952

J01789
J00955
J01071
J01993

J03061 J01444
J01456
J02880
J01611

J01438
J03079

J00865

J02390

J01170

J01074
J02915

J02921 J02318
J00746
J02743

J01992

J00488

J02896
J02869J02892
J01777

J02728

J02747 J02524 J02744J02733
J02097
J02740

J00542 J02371

J03075
J01116
J02871

J03089

J02843

J02587

J02687

J02927
J00145

J00460

J02741

J02558
J01077

J01035

J02027 J01542
J02255
J03131 J00298
J02732 J00732J00191
J01679
J03114
J01868
J01879
J01316
J02616
J02223
J02790

J02904
J01023

J02609

J02700

J01508

J02636

J02610
J00611

J01405

J02759

J02313

J02433

J01877

J02392

J02778

J00486

J02926

J02909

J02901

J01491

J00552

J01329

J02247

J01834

J01330

J02453

J02302

J01587

J02391

J02777

J00438

J02897

J02882

J02887

J01493

J00514

J01326

J01539

J01268

J00238

J01581

J02650

J00069

J02618

J01108

J02911

J00563

J00339

J02569

J01435

J02846

J02314

J01998

J01818

J02890

J00556

J00316

J00954

J00018

J02842

J02301

J00490

J01817

J00959

J01479

J02761

J00232

J01387

J02298

J01436

J01118

J01080

J00566

J01871
J00548

J01582

J02651

J00070

J02619

J02129

J01107

J02050

J02763

J02669

J02607

J02319

J01849

J01852
J00916

J01865

J02899

J02320

J00364

J01506

J00126
J01016

J00017

J00895
J01695

J00580

J02104

J00928
J00906

J03093
J02997
J02737

J00311
J01552
J00312
J00554

J02102
J01507

J02736
J02165

J02210

J01636
J01887

J01786

J02101

J02900

J02913

J02220
J01751

J00266

J01481

J03106

J00957

J02289
J02674

J02776

J00297

J00112

J01653
J02572
J01701 J01337
J02283J02290

J02930

J02857
J02271

J01454

J00415
J01504

J02466

J01001

J01265

J00247
J00900
J00899

J02360

J03017

J01513

J02912
J02894

J02760

J02142
J01631

J02002

J01607
J02836

J02564

J01037

J01939
J01938

J01519
J01590
J02234J02130 J01591
J01420
J02140
J01117
J03096
J00811
J02543
J02627 J01051
J01324
J01425
J02049 J01485
J01244 J01654
J01975 J02623J01483
J00021
J02825
J01842
J01972 J01509
J00322
J01138
J03024
J01922 J01650
J00192J01971
J00473
J02058 J00489
J01532J00259
J00862
J00918

J00216
J01931

J00024

J02973
J02615
J02956
J02410 J02407

J01078

J00361

J01081
J01380 J01417
J02182
J00108 J02378
J01954
J00522
J02552
J00685
J01131
J01753
J02605
J01990
J02056
J01752
J01737 J02603
J01040
J01373
J00857
J00133
J01741
J00391
J02585
J00368
J02088
J01099
J02646 J00340
J01953
J01814
J00866
J01661
J00792
J00961
J00568
J02622
J03002
J00198
J01757J00948
J02081 J02641
J02176
J02625
J00370
J00962
J02597J01143
J01823
J01733
J01147
J02814
J01763
J01142
J02928
J01211
J02015
J00681
J01853
J01738
J00669
J01522 J01624J00207J01348
J01742
J00267J00462
J01320 J00748
J00841J00424
J01328
J01124
J00103
J01525
J01520 J01521 J00341 J01482
J01215
J01963
J02376
J00668
J02164
J03126
J02883 J00197
J00837
J00092J00469
J02393
J02335 J01949
J02576
J02518
J01523
J01601
J00089
J00743
J02601
J01678
J02794
J00382 J01468
J02183
J01524 J00082
J02267
J02007
J01464
J02464
J00093
J02362
J02734
J02357
J02730
J00403 J02278
J00885
J02361
J02236
J00979J01977
J02227
J00090
J00487
J02746
J02600
J00500
J02249
J02163
J01745
J01052 J01750J01433
J00869
J00472 J01027
J02196
J02323
J01130
J00902
J02139
J03076
J02000
J00572
J01784
J01204
J00456
J00756J02691
J00091
J00921 J00945J01514
J03128
J00826
J02540
J01968
J01323
J02685
J00808
J01467
J02299 J02160
J02692
J00925
J02143
J01718
J00270
J02181 J01050
J01047
J01892
J00140 J00478
J00404
J00433
J00176J00274
J02516
J01041
J02631
J00532
J00289
J02377
J02975
J00336
J02212
J00359
J01533
J01046
J00480
J01543
J00479 J01221
J01560
J01974
J01819
J01873
J00751
J02235
J00028
J01076
J01860
J02815
J03072
J01275
J00278 J02954 J02689
J01338
J02109
J00186
J03046
J02534
J00730 J00937
J02578
J01962
J01828 J01538 J01385
J02748J02694
J01345
J00952
J02533 J02030
J01097
J01349
J02690
J00245
J02145
J00074
J02573
J02233
J00291
J01200
J01517
J01925
J02589
J00920
J01870
J00555
J00117
J02395
J01389
J02241
J03135 J01176
J02717
J02441
J01139
J00031
J01764
J00809 J01246
J01806
J00273J01978
J01290
J01840
J01423
J02307
J02086
J01558 J02499
J00474
J02111
J02394
J01426
J00080
J00296
J02562
J00030
J00830
J00328
J00015
J00315
J02277
J02783
J00098
J01157
J03006 J02438 J00546
J00436
J01350
J02442
J01959
J02953
J01478
J00785
J01346
J01284 J00816
J02722
J02199
J00588
J00573
J02200
J00308
J02547
J01144
J01294
J01134
J01146
J02291
J00465
J00537
J00251
J01859
J01714J00399
J02193
J01594
J00260 J00973
J02046
J01847
J00972
J01419
J01414
J00777 J01031
J01190
J02288
J03044
J01375
J02331
J00913
J01088
J02648
J02695
J02237 J01032
J02065
J00901
J02545
J01866
J02243
J02087
J02076J00530
J00388 J01299
J02982
J00338
J00760
J00853
J02423
J02668
J00269
J00452
J01383
J00982
J01381
J01960
J01603
J01627
J00418
J01950 J01734
J01158
J01788 J00819
J02711
J01357
J02131
J00704
J00858
J00127
J03127
J00293
J00867 J02577
J00849
J01098
J02106
J01461 J01736
J01512
J00981J00033
J01687
J01755
J02822
J02866
J01293
J00859
J01511
J02951
J01141
J01012
J01351
J02865
J02309
J00375
J02121
J02078
J02245
J01442
J02998
J00324
J02424
J01544
J01809
J01856
J02064
J01729
J02426
J01274
J01184
J02735
J00679
J00113
J00975
J01803
J02860
J02285
J01466
J00905
J02217
J01038
J00139
J01858
J00118
J01402
J01966
J02652
J01458
J01049
J00851
J02561 J02738
J02693
J01002
J02479
J02952
J02439
J01761
J03059
J00471
J01286
J02224
J00558
J01219
J00470
J01133 J01489
J02725
J02795
J02575
J00020
J02284
J00582
J00026
J01421
J01191
J01942
J02480
J01804
J00503
J02574
J01449
J00520
J00032
J00970
J00264
J00834
J02471
J00303
J02090
J00408
J01195
J02336
J01247
J00240
J02213
J01901
J02482
J03003
J00702
J02626
J03109
J01155
J02386
J00302
J02054
J01140
J02428
J02630
J03023
J00366
J03005
J02306
J02824
J00100
J01878
J02496
J01510
J01260
J00057
J02411
J00006
J01894
J01429
J00517
J02862
J02468
J02114
J03025 J01545
J01214
J00536
J02053
J02068
J00309
J02079J00313
J00755 J02489
J00277
J02168 J01243
J00539
J02221 J01222
J02414
J01287
J01024
J00544
J00908
J00323
J02067
J01749
J03047
J01236
J01196
J02009
J01979
J02910
J01768
J02514
J00848
J01206
J00599
J02461
J01712
J00618
J01447
J00012
J01754
J00184
J00144
J00282
J00583
J00695
J02451
J00444
J03065
J01340
J00608J00690
J00372
J01309
J02481
J02452
J00587
J00800
J01604
J00682
J02273
J01384
J02024
J00624
J01850
J01388
J00609
J02947J01307
J02582
J02567
J01756
J00376 J02447
J00281
J00019
J00389
J00065
J01055
J02292
J00936
J00499 J00446
J02444
J00620
J02057
J00623
J00594
J02755
J02436
J01746J00591
J01231J00223 J01566
J00745
J03104
J01178
J02520
J02170 J00935J00263 J01145 J00124 J01382
J00062
J00619
J01945 J01940
J02282
J00501J02008
J02863
J01606
J00025
J01790
J00653
J00295
J01710
J01857
J02296 J01798
J01880
J01312
J00173
J00670
J00058
J01103
J02001
J00244
J00045
J01413
J01487
J02643
J00252 J00457 J00696
J01534
J01239
J02055
J02492 J02820
J02226
J01484
J00387
J02443
J00330 J02440
J01547
J02269
J00225
J01676
J02353J00288
J01182
J01311
J02147
J01772
J02112
J01796
J02419
J01048
J02955
J01536
J01169
J02026
J00304 J01605
J01561
J01255
J00616
J01916
J00055
J02222
J00228
J02240
J02041
J02749 J02063
J00272
J02739
J01165
J02784
J02965
J00357
J00946
J01472
J02252
J02197
J00329
J02494
J01794
J00242
J01069
J02541
J00693
J01610 J01793
J02120 J01549
J01541
J01970
J02190
J00625
J00416
J01740
J00440
J01839
J00463
J02460
J00451
J00454
J02649
J01573
J00215
J00504
J02345
J00987
J02214
J00219
J00878
J00432
J00458
J01647
J00367
J00190J02016
J00224
J00061
J00437
J00081 J00482
J00775
J01807
J02051
J01967
J00445
J01396
J00220
J01802
J02312
J01775
J02264
J02796
J01808
J02254
J00011
J00776
J01706
J00974
J01713J01776
J00861
J00926
J00314
J00659
J00059
J02162
J02238
J02110
J01771
J01619
J03136
J01173
J02133
J02835
J00009 J02798
J00513J01153
J00046 J01769
J00079
J02583
J01072
J00739
J00002
J03016
J01792
J00708
J02454
J00723
J01920
J01692
J02495
J00860
J00703
J00194
J02563 J01183
J01882
J00731 J02177
J01632
J01172
J00665
J00722
J02676
J00453
J01943
J01783
J00812
J02617
J01663
J02253
J00222
J00320
J01440
J01774
J00673J02179
J00122
J00110
J00035
J01443
J01773
J02327
J00283
J02265
J01748
J00104
J01125
J02474
J02584
J00204
J00448
J02403J01708
J02174
J02475
J00005
J00106
J00036
J00601
J02571J02970
J00001
J02432
J02178
J02405
J00912
J00064
J01555
J00598
J00449
J01874
J01156
J02172
J00063
J00319
J00356
J01995
J02047
J01559
J01770
J02062
J00772
J00221
J02211
J01017
J00300
J02671
J00855 J00493
J00643
J00208
J00927
J02729
J00934
J01693
J00506J01355
J03107
J00076
J01779
J00666
J01918
J03045
J01159
J01273
J01465
J02108
J00645
J01427
J00044
J00519
J01937
J02231
J00136 J02488
J00196
J02490
J00610
J02347
J03009
J01717
J00740
J01535
J00822
J00790 J00773
J00188
J02418
J00138
J03105
J02590
J02966
J02498
J03034J02425
J00976
J02565
J02293
J01691
J01363
J01984
J00393
J02404
J03049
J02608 J01033
J02383 J00637
J02485
J00843
J01999 J02095
J02427
J01313
J03091
J00788
J00629
J00180
J01065
J02248
J03012
J01302
J02508
J02437
J01318
J01480J02279 J02731
J00603
J02580
J01248
J00716 J00664
J00774 J00764
J02964
J00802
J01356
J01288 J01556
J01229
J02367
J03053J00254
J02018
J03019
J01600
J00172
J00742
J00714
J01319J00491 J01929 J02118
J02113
J02209
J02602
J01720
J00825
J00717
J01422
J00193
J02080
J02832
J01843
J01895
J02962
J00229
J02219
J03013
J02902
J03007J00721
J00750
J00595 J00877
J03020
J01272 J00401
J01276
J01404
J01018
J00016
J02281
J03138
J02266
J02523
J00715 J00718
J03031
J03050
J00202
J02817
J02208
J01732 J01722
J00719
J02519 J02532J00676
J00782
J02446
J01043
J00694
J03018 J03078
J00729
J01890
J00010
J01366
J02295
J00720
J01416
J03055
J00727
J00903
J03051
J00231
J00724
J02251
J01462
J01486
J02048
J01593
J02099
J02363
J01296
J01410
J00607 J00725
J00481
J02467
J02978
J00585 J00944
J00968
J01085
J02559J01174
J00799
J00169
J02201
J00856 J01015
J00767
J00602
J02094
J00515
J00392
J00398
J00780
J00680
J00754
J00586
J03092
J00757
J02581 J01957
J00672 J02180J00299
J00789
J00651
J01277 J00850 J00647
J03038
J02891
J02476
J00151 J00551 J01743
J02073
J00109
J00152
J00761
J00655
J02985
J01985
J00649
J00421 J00590 J01719
J01572J02297
J00931
J01622
J01110 J02592
J00793 J02645J00763
J03112
J02677
J00386
J00701
J00013
J00390
J01042
J02531J00634
J00626
J02473J03054
J01278 J02415
J00779
J02458
J03110
J02713J03063J00632
J00014
J01150
J02429
J01009
J00038J00265
J00029
J01677J00378
J02330 J01585
J00674
J00007J00593 J01765 J01282 J00644J02430
J02708
J00712
J01730
J02072
J03026
J01135
J02215
J02449
J02710
J00597
J00084
J02781 J00915
J00781
J01475
J01474
J01062
J02937 J00212
J00969
J02501
J01109
J02280
J02680
J01723
J03057
J01904
J00210
J00950
J02052
J01986
J02448
J01280
J00964
J00394
J01249J00661
J00648
J00137
J00049
J03121
J00688
J00034
J00290
J00003
J01642
J02935
J00041
J01343 J02420
J00425
J03021
J01707
J01956
J01409
J00371
J00574
J03037
J00327
J02369
J01066
J03039
J00008J00787
J01731
J02368
J02491
J00352
J02294
J01073
J00963J02503
J00342
J03008
J00827 J00606
J00753
J00373
J02455
J01218
J02134
J01694
J00700
J01010
J01149J02477J01014
J03043
J00689
J00671J00794
J00951
J00294
J00864
J00768
J02457
J02976
J00677
J01724
J00575
J00200
J00369
J00206
J00037
J00650J02478
J01412J01283
J00692
J00641
J01965
J00004 J01390
J00874
J01151
J00863J03052
J01285
J02003
J01008
J01019
J00971
J00083
J01471
J02191
J00786
J00383
J00461
J01075
J01715 J01264
J01105
J01132
J00405
J02192
J01056
J01810
J02421
J00662
J00890
J01980
J02013 J01223 J00099
J00839
J02365
J02333
J00875
J01194
J00886
J01224
J01958
J02070
J02242
J01197
J01129
J00956J01201 J01039J00784 J00657
J00663
J01997
J01557
J02286
J01951
J01216 J03130
J00642
J00654
J00783
J02936
J03010
J00765
J02250
J01136
J01621
J01011
J01220
J02768J00984
J00589
J02642
J02799J00791
J01114
J02229
J02785
J02681
J00660
J00459J01811
J01198
J01192J01917
J00996J02644
J00143
J02704 J00914 J02074
J02697
J01716
J00248
J00175
J03022
J01203
J00167
J03048
J01154
J01537 J01060
J00965
J02154
J00360
J02435
J02517 J00377
J01162
J02275
J01199 J01202
J00276
J01235J00778J00168
J00636
J00023
J03113
J00728
J01208 J01281
J02673
J00420
J02931 J01232
J01864
J00628
J00508
J01418
J00638
J01310
J02664
J02325J01562
J02720 J01919J02780
J00807
J00880
J01036J02536J02004 J00604
J03027
J01623
J00737 J02971
J01205
J02194
J00846
J00726
J01596 J00435
J02355
J02806 J00796 J02100J03123
J01930
J00271
J03064
J00374
J01228
J01568
J01955 J02132
J01321
J01322
J02707
J00709
J01029
J00922
J01251
J01682
J00114 J01187
J02346
J00105
J01899
J02797
J00991
J00630
J02324
J02967
J01263
J02972
J01936
J01230
J01973
J01897J01898
J01207
J02509
J03108
J01902J01886
J02169
J02527
J02216J01928
J00923
J02380
J00205
J02555
J00828
J00770
J00467J01185
J02827
J00639
J00627
J01317
J03122
J01007
J03040
J00498
J00142
J01334
J01209J01006
J01459
J02665
J02061
J00022
J02819
J02409
J00417
J01148
J00997 J00410
J00505
J00771 J02511
J01473
J00845
J00179J02228
J02225
J00917
J02184
J01161
J02762
J02807
J00707
J01812
J01805
J00621
J02542
J00174
J00824
J01415
J00998
J00492J02014
J00406
J01574
J00384
J01709
J00687
J00622 J02069
J02483
J02493
J01226
J02556
J01137
J02406
J02397
J00759
J00430 J03029
J00884
J00135
J01352
J02566
J00596
J01670J01635
J01257
J01913
J00656
J01727 J01914
J01656 J02987
J02166
J03000
J00842
J01306
J00631
J00358
J02115
J00600
J01445J01657
J00484
J01637
J01259
J00107
J01739
J02672
J00419
J00187
J02510
J02683
J00134J00801 J00947
J00698
J01639
J02122
J01068
J00678
J00039
J00584
J01241
J01238
J00887 J01460
J00699
J02328
J02060
J02943 J03133
J00983
J00882
J00429
J02614
J01924
J03028 J01057
J00095
J00195
J01477
J02186
J01988
J00667
J02141
J00362
J01815
J02544
J01655
J03058
J00199J02329
J00253
J01791 J00125 J00097
J02158
J02756
J02535
J00683
J02107
J00178
J02688
J00334
J00941
J03103
J00617
J01530 J01923
J00686
J01269
J01893
J02206
J00691
J00365
J02588
J02144
J01291J01526
J00605
J01987
J02981
J00938
J01463
J02372
J00249
J02818
J00847
J00101
J00078J02959 J01903
J02188 J00883
J01711
J00056
J00896J01664
J01166
J01640
J02714
J00257
J01626
J02942
J02434
J01408
J00094 J00189 J00301
J00164 J02398
J02632
J01262
J01799
J02634
J01797 J02611J02276
J01305
J01766
J01634
J00203
J02272J00218 J02375 J00335
J00752
J00579
J00705
J02119
J00096
J01867
J01584
J02804
J00747
J02538
J00929
J02684
J00413
J00166
J01102
J01672
J01906
J01597J02996
J00414
J03137
J00889
J02639
J02696
J01531
J02960J03134
J00395
J02961
J01896
J03090
J02934
J01374
J01163
J01567
J02679
J00409
J02105
J01969
J00379
J00214
J02816
J00876
J01497 J01641
J02389
J01376
J02939
J00881
J02218
J00510
J02635
J02640
J00292
J00578
J02637
J00911
J00321
J02500
J01674
J02171
J00275
J01667
J02167
J01900
J01167
J01912
J01915
J01976
J02093
J01671
J00141
J02878
J02703
J01115
J02431
J02066
J01638 J02116
J01371
J00246
J00888
J02006
J02718
J01787
J01964
J02920
J03032
J00684
J02303
J02189J02268
J00262
J01602
J02528
J01884
J02554
J01013
J01885 J02019
J03117
J03030
J01726
J00949
J02040
J02359
J02905
J02012
J02005
J01455
J00350
J01341
J03116
J00960
J02198
J00060
J03120
J02522
J01164
J01800 J01430
J00560
J01469
J02539
J01227
J01451
J00171 J01586
J02702
J00502 J00762
J01054
J02071
J00978
J01592
J02232
J02472
J00810
J01841
J02381
J02546
J01403
J02932
J01267
J03119
J01875
J00261
J03041
J02723
J00243
J01668J01665
J02525
J00999
J01369
J02940J01673
J00279
J01045
J02098
J02338
J01659
J02633
J00795
J00407
J00710
J01314
J00285
J01833
J00353
J01666
J01490 J02701
J00111
J00345
J02091
J01344
J01829
J00354
J00318
J02470
J01669
J02148 J00518
J00344
J00990
J01020J02356
J01450
J02938
J00910
J01189
J01889
J02146
J01005 J01629
J02548
J02385
J00170
J00343
J00613
J02570
J00658
J02657
J01888
J00349
J00511
J00346
J00994
J02979
J01905
J01926
J01399J01400
J01625
J00496
J00675
J00988
J01696
J01747J00347
J02752
J00907
J02873
J01448
J01392
J01298
J03125
J00529
J00236
J00823
J00351
J02895
J02526
J01398
J00165 J01725
J01044
J02549
J01991
J00464
J01554J00525
J00348
J01266 J02715
J00048
J00201J00412
J00495
J00838
J03033
J01406
J00494
J02195
J01240
J01386
J01171
J02413
J02537
J00363 J02459
J02686
J01401
J02161J03062
J02502
J00162
J00533
J00989
J02462
J00380 J01303
J02594
J02010
J01883
J00255
J01160
J00434
J01295
J01411
J00280
J00234
J02020
J00942
J00531
J01407
J02507
J01034
J00317 J01358
J01452
J00402 J01225
J01210
J01744
J01907
J01499
J02716
J00443
J01911
J02593
J00235
J02974
J00209
J02487
J01760
J00873
J02867 J02881
J02486
J00333
J02712
J02207
J02742
J02011
J00241
J01515
J02484
J01989
J00527
J02370J03060
J02872J02841
J01881
J03056
J01300
J01646
J00526
J02724
J00571
J01021
J00766
J02727
J01630
J01439
J02021
J01101
J02579
J00904

J02721

J00422

J02629

J02638

J02834

J01061

J03102

J02958

J00177
J02017

J00516 J02553

J01175J01453

J01844
J02311

J01935

J01932

J00040

J01652
J01728

J00932

J01941
J00523
J01660

J00769

J01437

J01119

J02675
J01872

J01981
J02745

J01758
J01982

J02709
J01759

J01996
J02719

J02023

J01082

Figure 2: he network consisting of 0.7% Jamu pairs (correlation value above or equal to 0.596).

J02096

J02667

J00615

J00697

6

BioMed Research International
Table 3: Statistics of three datasets.
Parameters

Network
statistics

DPClusO

Total pairs
Minimum correlation
Number of Jamu formulas
Average degree
(Random network: ER)
(Random network: BA)
(Random network: CNN)
Clustering coeicient
(Random network: ER)
(Random network: BA)
(Random network: CNN)
Number of connected components
(Random networks: ER, BA, CNN)
Network diameter
(Random network: ER)
(Random network: BA)
(Random network: CNN)
Network density
(Random network: ER)
(Random network: BA)
(Random network: CNN)
Total number of clusters
Number of clusters with more than 2 Jamu
(%)
Number of Jamu formulas in the biggest cluster

of all formulas consists of 465 plants. We assigned 3138 Jamu
formulas to 116 diseases of International Classiication of
Diseases (ICD) version 10 from World Health Organization
(WHO, Table 1) [19]. hose 116 diseases are mapped to
18 classes of disease, which contains 16 classes of disease
from National Center for Biotechnology Information (NCBI)
[20] and 2 additional classes. Table 2 shows distribution
of 3138 Jamu into 18 classes of disease. According to this
classiication, most Jamu formulas are useful for relieving
muscle and bone, nutritional and metabolic diseases, and
the digestive system. Furthermore, there is no Jamu formula
classiied into glands and hormones and neonatal disease
classes. We excluded 4 Jamu formulas which are used to treat
fever in the evaluation process because this symptom is very
general and almost appeared in all disease classes. Jamuplant-disease relations can be represented using 2 matrices:
irst matrix is Jamu-plant relation with dimension 3138 ×
465 and the second matrix is Jamu-disease relation with
dimension 3138 × 18.
Ater completion of data acquisition process, we calculated the similarity between Jamu pairs using correlation
measure. he similarity measures between Jamu pairs were
determined based on their ingredients. Corresponding to �
(3138 in present case) Jamu formulas, there can be maximum
(� × (� − 1)/2) = (3138 × (3137/2)) = 4,921,953 Jamu

0.7%

0.5%

0.3%

34,454
0.596
2,779
24.8
(24.8 ± 0.0)
(24.7 ± 0.1)
(24.7 ± 0.4)
0.521
(0.009 ± 0.000)
(0.030 ± 0.001)
(0.246 ± 0.008)
69
(1)
15
(4.0 ± 0.0)
(10.8 ± 0.8)
(14.6 ± 1.9)
0.008
(0.009 ± 0.000)
(0.009 ± 0.000)
(0.009 ± 0.000)

24,610
0.665
2,496
19.7
(19.7 ± 0.0)
(19.7 ± 0.1)
(19.7 ± 0.4)
0.520
(0.008 ± 0.000)
(0.028 ± 0.001)
(0.239 ± 0.008)
119
(1)
17
(4.0 ± 0.0)
(11.2 ± 1.5)
(14.1 ± 1.4)
0.008
(0.008 ± 0.000)
(0.008 ± 0.000)
(0.008 ± 0.000)

14,766
0.718
2,085
14.2
(14.2 ± 0.0)
(14.1 ± 0.1)
(14.0 ± 0.4)
0.540
(0.007 ± 0.000)
(0.026 ± 0.001)
(0.233 ± 0.010)
254
(1)
20
(5.0 ± 0.0)
(10.8 ± 0.9)
(14.7 ± 1.3)
0.007
(0.007 ± 0.000)
(0.007 ± 0.000)
(0.007 ± 0.000)

1,746
1,296
(74.2)
118

1,411
873
(61.9)
104

938
453
(48.3)
89

pairs. We sorted the Jamu pairs based on correlation value
using descending order and selected top-� (0.7%, 0.5%,
and 0.3%) pairs of Jamu formula to create 3 sets of Jamu
pairs. he number of Jamu pairs for 0.7%, 0.5%, and 0.3%
datasets is 34,454 pairs, 24,610 pairs, and 14,766 pairs and
the corresponding minimum correlation values are 0.596,
0.665, and 0.718, respectively. he three datasets of Jamu
pairs can be regarded as three undirected networks (step 1 in
Figure 1) consisting of 2779, 2496, and 2085 Jamu formulas,
respectively (Table 3). Figure 2 shows visualization of 0.7%
Jamu networks using Cytoscape Spring Embedded layout. We
veriied that the degree distributions of the Jamu networks
are somehow close to those of scale-free networks, that is,
roughly are of power law type. However, in the high-degree
region the power law structure is broken (Figure 3). Nearly
accurate relation of power laws between medicinal herbs
and the number of formulas utilizing them was observed in
Jamu system but not in Kampo (Japanese crude drug system)
[4]. he diference of formulas between Jamu and Kampo
can be explained by herb selection by medicinal researchers
based on the optimization process of selection [4]. hus,
the broken structure of power law corresponding to Jamu
networks is associated with the fact that selection of Jamu
pairs based on ingredient correlation leads to nonrandom
selection. We also constructed random networks according

BioMed Research International

7

●
●
●●
● ●
●●
●●
●
●●●●
●
●

●●
●

●

10

20

50

Frequency
13 23 40
8
5
2 3

●
●

1

37
22
13
5
2
1

3

●

●
●●
●
●
●
●
●●● ●●

5

●
● ●
● ●●
●
●
●
● ●
●
●●
●
●
●● ● ●
●
●
●
●●● ●
●
●
●
●
●● ● ●
●
●● ● ● ●●●●
●
●●
●●
●●
●
●●●●●●● ●●
●●●
●
●

71

●

●● ●
●
●
●
●

2

●

●

●
●
●●
●
●●
●●
●●
● ●●
● ●●
●● ●
●●● ●
●●
● ●
● ● ●
● ● ●
●
●●●●● ● ●
●
●
● ●● ●
●
●●
●
●
●●●●
● ●● ●

1

●

221

●

8

Frequency

0.5%
●

●

63 114

234

0.7%

200

100

1

2

5

10

(Deg.)

20
(Deg.)

●

●●
●●●
●
●●
●●●●●
●●

50

100

200

0.3%
●
●
●
●
●

81

●
●●

●

Frequency
12 22 43

● ●
●
●
●●●
●
●
●
●

7

●

●●
●

●

●

●

4

●
●

●
● ●● ●

2

●● ●●
● ●

1

●●
●●

1

2

5

10
(Deg.)

20

●●
●
●

50

100

Figure 3: Degree distributions of three Jamu networks roughly follow power law. he �-axis corresponds to the log of degree of a node in the
Jamu network and the �-axis corresponds to the log of the number of Jamu.

to Erdős-Rényi (ER) model [21], Barabási-Albert (BA) model
[22], and Vazquez’s Connecting Nearest Neighbor (CNN)
model [23] of the same size corresponding to each of the real
Jamu network. We used Cytoscape Network Analyzer plugin
[24] and R sotware for analyzing the characteristics of both
the Jamu and the random networks.
We determined ive statistical indexes, that is, average
degree, clustering coeicient, number of connected component, network diameter, and network density of each Jamu
network and also of each random network. he clustering
coeicient �� of a node � is deined as �� = 2�� /(�� (�� − 1)),
where �� is the number of neighbors of � and �� is the number
of connected pairs between all neighbors of �. he network
diameter is the largest distance between any two nodes. If

a network is disconnected, its diameter is the maximum of all
diameters of its connected components. A network’s density
is the ratio of the number of edges in the network over the
total number of possible edges between all pairs of nodes
(which is �(� − 1)/2, where � is the number of vertices, for
an undirected graph). he average number of neighbors and
the network density are the same for the real and random
networks of the same size as it is shown in Table 3. In case
of 0.7% and 0.5% real networks, the clustering coeicient is
roughly the same and in case of 0.3% the clustering coeicient
is somewhat larger. he number of connected components
and the diameter of the Jamu networks gradually decrease
as the network grows bigger by addition of more nodes and
edges.

BioMed Research International
300

300
Number of clusters

Number of clusters

8

200
100

200
100

0

0
0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4
0.6
Matching score

Matching score
(a) 0.7%

0.8

1.0

(b) 0.5%

Number of clusters

300
200
100
0
0.0

0.2

0.4

0.6

1.0

0.8

Matching score
(c) 0.3%

Figure 4: Distribution of clusters based on matching score.
150
Number of predicted plants

Ratio of number of clusters to
total clusters

1.0
0.8
0.6
0.4
0.2
0.0

100

50

0
0.0

0.1

0.2

0.3 0.4 0.5 0.6 0.7
Matching score threshold

0.8

0.9

1.0

0.7%
0.5%
0.3%

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Matching score threshold
0.7%
0.5%
0.3%

(a)

(b)

Figure 5: (a) Success rate and (b) number of predicted plants with respect to matching score thresholds.

Very diferent values corresponding to clustering coeficient, connected component, and network diameter imply
that the Jamu networks are quite diferent from all 3 types
of random networks. he diferences between Jamu networks
and ER random networks are the largest. Random networks
constructed based on other two models are also substantially
diferent from Jamu networks. Based on the fact that the
random networks constructed based on all three types of
models are diferent from the Jamu networks, it can be
concluded that structure of Jamu networks is reasonably
biased and thus might contain certain information about

plant-disease relations. Specially, much higher value corresponding to clustering coeicient indicates that there are
clusters in the networks worthy to be investigated. To extract
clusters from the Jamu networks (step 2 in Figure 1) we
applied DPClusO network clustering algorithm [14] to generate overlapping clusters based on density and periphery
tracking.
3.2. Supervised Clustering Based on DPClusO. DPClusO is a
general-purpose clustering algorithm and useful for inding
overlapping cohesive groups in an undirected simple graph

BioMed Research International

9

Table 4: List of plants assigned to each disease.

Table 4: Continued.

Number Plants name
A. Disease: blood and lymph diseases

Hit-miss status

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

Tamarindus indica
Allium sativum
Tinospora tuberculata
Piper retrofractum
Syzygium aromaticum
Bupleurum falcatum
Graptophyllum pictum
Plantago major
Zingiber oicinale
Cinnamomum burmannii
Soya max
Kaempferia galanga
Curcuma longa
Piper nigrum
Zingiber aromaticum
Phyllanthus urinaria
Oryza sativa
Myristica fragrans
Alstonia scholaris
Syzygium polyanthum
Andrographis paniculata
Sida rhombifolia
Cyperus rotundus
Sonchus arvensis
Curcuma aeruginosa
Curcuma xanthorrhiza
B. Disease: cancers

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Miss
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Miss
Hit
Miss
Hit
Miss
Hit
Hit

1

Catharanthus roseus
C. Disease: the digestive system

Hit

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Foeniculum vulgare
Glycyrrhiza uralensis
Imperata cylindrica
Zingiber purpureum
Physalis peruviana
Punica granatum
Echinacea purpurea
Zingiber oicinale
Psidium guajava
Baeckea frutescens
Amomum compactum
Cinnamomum burmannii
Melaleuca leucadendra
Caesalpinia sappan
Parkia roxburghii
Rheum tanguticum
Kaempferia galanga
Coriandrum sativum

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

∗
∗
∗
∗

∗
∗
∗
∗
∗
∗
∗
∗
∗

∗

∗
∗
∗
∗
∗
∗
∗

Number Plants name

Hit-miss status

19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42

Curcuma longa
Zingiber aromaticum
Phyllanthus urinaria
Myristica fragrans
Hydrocotyle asiatica
Carica papaya
Mentha arvensis
Lepiniopsis ternatensis
Helicteres isora
Andrographis paniculata
Symplocos odoratissima
Schisandra chinensis
Blumea balsamifera
Silybum marianum
Cinnamomum sintoc
Elephantopus scaber
Curcuma aeruginosa
Kaempferia pandurata
Curcuma xanthorrhiza
Curcuma mangga
Curcuma zedoaria
Daucus carota
Matricaria chamomilla
Cymbopogon nardus
D. Disease: female-speciic diseases

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

Foeniculum vulgare
Imperata cylindrica
Tamarindus indica
Pluchea indica
Piper retrofractum
Punica granatum
Uncaria rhynchophylla
Zingiber oicinale
Guazuma ulmifolia
Nigella sativa
Terminalia bellirica
Baeckea frutescens
Phaseolus radiatus
Amomum compactum
Sauropus androgynus
Usnea misaminensis
Cinnamomum burmannii
Melaleuca leucadendra
Parameria laevigata
Parkia roxburghii
Piper cubeba
Kaempferia galanga

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

∗

∗

∗
∗
∗
∗

∗

∗

∗

10

BioMed Research International
Table 4: Continued.

Table 4: Continued.

Number Plants name

Hit-miss status

Number Plants name

Hit-miss status

23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

Coriandrum sativum
Kaempferia angustifolia
Curcuma longa
Zingiber aromaticum
Languas galanga
Galla lusitania
Quercus lusitanica
Hydrocotyle asiatica
Areca catechu
Lepiniopsis ternatensis
Helicteres isora
Piper betle
Elephantopus scaber
Kaempferia pandurata
Curcuma xanthorrhiza
Sesbania grandilora
E. Disease: the heart and blood vessels

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

1
2
3
4
5
6
7
8
9
10

Allium sativum
Curcuma longa
Morinda citrifolia
Homalomena occulta
Hydrocotyle asiatica
Alstonia scholaris
Syzygium polyanthum
Andrographis paniculata
Apium graveolens
Imperata cylindrica
F. Disease: male-speciic diseases

Hit
Hit
Hit
Hit
Hit
Hit
Miss
Hit
Miss
Hit

1
2
3
4
5
6

Cucurbita pepo
Serenoa repens
Baeckea frutescens
Phaseolus radiatus
Curcuma longa
Elephantopus scaber
G. Disease: muscle and bone

Miss
Miss
Hit
Hit
Hit
Hit

14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

1
2
3
4
5
6
7
8
9
10
11
12
13

Foeniculum vulgare
Clausena anisum-olens
Zingiber purpureum
Allium sativum
Strychnos ligustrina
Tinospora tuberculata
Piper retrofractum
Syzygium aromaticum
Cola nitida
Ginkgo biloba
Panax ginseng
Equisetum debile
Zingiber oicinale

Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit
Hit

∗
∗

∗
∗
∗
∗
∗
∗

∗

∗

∗
∗
∗

Ganoderma lucidum
Nigella sativa
Terminalia bellirica
Baeckea frutescens
Amomum compactum
Cinnamomum burmannii
Melaleuca leucadendra
Parameria laevigata
Psophocarpus tetragono