Research Article Supervised Clustering Based on DPClusO ...downloads.hindawi.com/journals/bmri/2014/831751.pdf · Research Article Supervised Clustering Based on DPClusO: Prediction

Research ArticleSupervised Clustering Based on DPClusO:Prediction of Plant-Disease Relations Using JamuFormulas 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, Japan2Department of Computer Science, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia3 Department of Biochemistry, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia4Department of Statistics, Bogor Agricultural University, Kampus IPB Dramaga, Jl. Meranti, Bogor 16680, Indonesia5 Biopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jl. Taman Kencana No. 3,Bogor 16151, Indonesia

Correspondence should be addressed to Shigehiko Kanaya; [email protected]

Received 30 November 2013; Accepted 18 February 2014; Published 7 April 2014

Academic Editor: Samuel Kuria Kiboi

Copyright © 2014 Sony Hartono Wijaya et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Indonesia has the largest medicinal plant species in the world and these plants are used as Jamu medicines. Jamu medicines arepopular traditional medicines from Indonesia and we need to systemize the formulation of Jamu and develop basic scientificprinciples of Jamu to meet the requirement of Indonesian Healthcare System. We propose a new approach to predict the relationbetween plant and disease using network analysis and supervised clustering. At the preliminary step, we assigned 3138 Jamuformulas to 116 diseases of International Classification of Diseases (ver. 10) which belong to 18 classes of disease from NationalCenter for Biotechnology Information. The correlation measures between Jamu pairs were determined based on their ingredientsimilarity. Networks are constructed and analyzed by selecting highly correlated Jamu pairs. Clusters were then generated by usingthe network clustering algorithm DPClusO. By using matching score of a cluster, the dominant disease and high frequency plantassociated to the cluster are determined. The plant to disease relations predicted by our method were evaluated in the context ofpreviously published results and were found to produce around 90% successful predictions.

1. Introduction

Big data biology, which is a discipline of data-intensivescience, has emerged because of the rapid increasing ofdata in omics fields such as genomics, transcriptomics,proteomics, and metabolomics as well as in several otherfields such as ethnomedicinal survey. The number of medic-inal plants is estimated to be 40,000 to 70,000 around theworld [1] and many countries utilize these plants as blendedherbal medicines, for example, China (traditional Chinesemedicine), Japan (Kampo medicine), India (Ayurveda, Sid-dha, and Unani), and Indonesia (Jamu). Nowadays, the use

of traditional medicines is rapidly increasing [2, 3]. Thesemedicines consist of ingredients made from plants, animals,minerals, or combination of them.The traditional medicineshave been used for generations for treatments of diseasesor maintaining health of people and the most popular formof traditional medicine is herbal medicine. Blended herbalmedicines as well as single herb medicines include a largenumber of constituent substances which exert effects onhuman physiology through a variety of biological pathways.The KNApSAcK Family database systems can be used tocomprehensively understand the medicinal usage of plantsbased upon traditional and modern knowledge [4, 5]. This

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014, Article ID 831751, 15 pageshttp://dx.doi.org/10.1155/2014/831751

2 BioMed Research International

Table 1: List of diseases using International Classification of Dis-eases ver. 10 (class of disease IDs correspond to Table 2).

ID Disease Class ofdisease

1 Abdominal pain 32 Abdominal pain, diarrhea 33 Acne 164 Acne, skin problems (cosmetics) 165 Amenorrhoea, dysmenorrhea 66 Amenorrhoea, irregular menstruation 67 Anaemia 18 Appendicitis, urinary tract infection, tonsillitis 39 Arthralgia 1110 Arthralgia, arthritis 1111 Asthma 1512 Benign prostatic hyperplasia (Bph) 1013 Breast disorder 614 Bromhidrosis 1615 Bronchitis 1516 Cancer 217 Cancer pain 218 Cancer, inflammation 219 Colic abdomen, bloating (in infant) 320 Common cold 1521 Common cold, dyspepsia, insect bites 15, 3, 1622 Common cold, influenza 1523 Cough 1524 Degenerative disease 1425 Dermatitis, urticaria, erythema 1626 Diabetes 1427 Diabetic gangrene 1628 Diarrhea 329 Diarrhea, abdominal pain 330 Diseases of the eye 531 Disorders in pregnancy 632 Dysmenorrhea 633 Dysmenorrhea, irregular menstruation 634 Dysmenorrhea, menstrual syndrome 635 Dyspepsia 336 Dyspnoea 1537 Dyspnoea, cough, orthopnoea 1538 Fatigue 1139 Fatigue, anaemia, loss appetite 140 Fatigue, lack of sexual function 641 Fatigue, low back pain 1142 Fatigue, myalgia, arthralgia 1143 Fatigue, osteoarthritis 1144 Fertility problem 6, 1045 Fever 0

Table 1: Continued.


46 Gastritis, gastric ulcer 347 Haemorrhoids 148 Headache 1349 Heart diseases 850 Heartburn 3, 851 Hepatitis, other diseases of liver 352 Hypercholesterolaemia 1453 Hypertension 854 Hypertension, diabetes 1455 Hypertension, hypercholesterolaemia 1456 Hyperuricemia 157 Immunodefficiency 958 Indigestion (K.30) 359 Indigestion, lose appetite 360 Infertility 6, 10

61 Irregular menstruation, menstruationsyndrome 6

62 Kidney diseases 1763 Lactation problems 664 Leukorrhoea (Vaginalis) 665 Leukorrhoea (Vaginalis), dysmenorrhoea 666 Lose appetite 367 Lose appetite, underweight 1468 Low back pain, myalgia, arthralgia 1169 Low back pain, myalgia, constipation 1170 Low back pain, urinary tract infection 1771 Lung diseases 1572 Malaise and Fatigue 1173 Malaise and Fatigue, Constipation 1174 Malaise and Fatigue, Fertility Problems 10, 1175 Malaise and Fatigue, Low Back Pain 1176 Malaise and Fatigue, Sexual Dysfunction 11, 6, 10

77 Malaise and Fatigue, Skin Problems(Cosmetics) 16

78 Malaria, anaemia 179 Meno-metrorrhagia 680 Menopausal syndrome 6

81 Menopause/menstrual syndrome, leukorrhoea(vaginalis) 6

82 Menstrual syndrome 683 Menstrual syndrome, fatigue 684 Migraine 1385 Mood disorder 1886 Myalgia, arthralgia 1187 Nausea/vomiting of pregnancy 688 Osteoarthritis 1189 Osteoarthritis, fatigue 11

BioMed Research International 3

Table 1: Continued.


90 Overweight, obesity 1491 Paralysis 1392 Post partum syndrome 693 Prevent from overweight 1494 Respiratory infection due to smoking 1595 Respiratory tract infection 1596 Rheumatoid arthritis, gout 1197 Secondary amenorrhea 698 Secondary amenorrhea, irregular menstruation 699 Sexual dysfunction, fatigue 6, 10100 Skin diseases 16101 Skin problems (cosmetics) 16102 Sleeping and Mood Disorders 18103 Sleeping disorders 18104 Stomatitis 3105 Stomatitis, gingivitis, tonsilitis 3106 Stone in kidney (N20.0) 17

107 Stone in kidney (N20.0), urinary bladder stone(N21.0) 17

108 Tonsilitis 4109 Tonsilofaringitis 4110 Toothache 13111 Typhoid, dyspepsia 3112 Ulcer of anus and rectum 3113 Underweight, lose appetite 3114 Urinary tract infection (urethritis) 17115 Vaginal discharges 6116 Vaginal diseases 6

database has information about the selected herbal ingre-dients, that is, the formulas of Kampo and Jamu, omicsinformation of plants and humans, and physiological activ-ities in humans. Jamu is generally composed based on theexperience of the users for decades or even hundreds ofyears. However, versatile scientific analyses are needed tosupport their efficacy and their safety. Attaining this objectiveis in accordance with the 2010 policy of the Ministry ofHealth of Indonesian Government about scientification ofJamu. Thus, it is required to systemize the formulationsand develop basic scientific principles of Jamu to meet therequirement of Indonesian Healthcare System. Afendi et al.initiated and conducted scientific analysis of Jamu for findingthe correlation between plants, Jamu, and their efficacy usingstatistical methods [6–8]. They used Biplot, partial leastsquares (PLS), and bootstrapping methods to summarize thedata and also focused on prediction of Jamu formulations.These methods give a good understanding about relationshipbetween plants, Jamu, and their efficacy. Among 465 plantsused in 3138 Jamu, 190 plants were shown to be effectivefor at least one efficacy and these plants were considered

to be the main ingredients of Jamu. The other 275 plantsare considered to be supporting ingredients in Jamu becausetheir efficacy has not been established yet.

Network biology can be defined as the study of thenetwork representations of molecular interactions, both toanalyze such networks and to use them as a tool to makebiological predictions [9]. This study includes modelling,analysis, and visualizations, which holds important task inlife science today [10]. Network analysis has been increasinglyutilized in interpreting high throughput data on omics infor-mation, including transcriptional regulatory networks [11],coexpression networks [12], and protein-protein interactions[13]. We can easily describe relationship between entities inthe network and also concentrate on part of the networkconsisting of important nodes or edges.These advantages canbe adopted for analyzing medicinal usage of plants in Jamuand diseases. Network analysis provides information aboutgroups of Jamu that are closely related to each other in termsof ingredient similarity and thus allows precise investigationto relate plants to diseases. On the other hand, multivariatestatisticalmethods such as PLS can assign plants to efficacy byglobal linear modeling of the Jamu ingredients and efficacy.However, there is still lack of appropriate network basedmethods to learn how and why many plants are grouped incertain Jamu formula and the combination rule embeddingnumerous Jamu formulas.

It is needed to explore the relationship between Indone-sian herbal plants used in Jamu medicines and the diseaseswhich are treated using Jamu medicines. When effectivenessof a plant against a disease is firmly established, then furtheranalysis about that plant can be proceeded to molecular levelto pinpoint the drug targets. The present study developeda network based approach for prediction of plant-diseaserelations. We utilized the Jamu data from the KNApSAcKdatabase. A Jamu network was constructed based on thesimilarity of their ingredients and then Jamu clusters weregenerated using the network clustering algorithm DPClusO[14, 15]. Plant-disease relations were then predicted by deter-mining the dominant diseases and plants associated withselected Jamu clusters.

2. Methods

2.1. Concept of the Methodology. Jamu medicines consistof combination of medicinal plants and are used to treatversatile diseases. In this work we exploit the ingredientsimilarity between Jamu medicines to predict plant-diseaserelations. The concept of the proposed method is depictedin Figure 1. In step 1 a network is constructed where a nodeis a Jamu medicine and an edge represents high ingredientsimilarity between the corresponding Jamu pair. In Figure 1,the nodes of the same color indicate the Jamumedicines usedfor the same disease.The similarity is represented by Pearsoncorrelation coefficient [16, 17]; that is,

corr (𝑋, 𝑌) =∑𝑙

𝑖=1(𝑥𝑖 − 𝑥) (𝑦𝑖 − 𝑦)

√∑𝑙

𝑖=1(𝑥𝑖 − 𝑥)

2

∑𝑙

𝑖=1(𝑦𝑖 − 𝑦)

2, (1)


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

ID Class of disease (NCBI) Ref. Number of Jamu Percentage1 Blood and lymph diseases NCBI 201 6.412 Cancers NCBI 32 1.023 The digestive system NCBI 457 14.564 Ear, nose, and throat NCBI 2 0.065 Diseases of the eye NCBI 1 0.036 Female-specific diseases NCBI 382 12.177 Glands and hormones NCBI 0 —8 The heart and blood vessels NCBI 57 1.829 Diseases of the immune system NCBI 22 0.7010 Male-specific diseases NCBI 17 0.5411 Muscle and bone NCBI 649 20.6812 Neonatal diseases NCBI 0 —13 The nervous system NCBI 32 1.0214 Nutritional and metabolic diseases NCBI 576 18.3615 Respiratory diseases NCBI 313 9.9716 Skin and connective tissue NCBI 163 5.1917 The urinary system ∗ 90 2.8718 Mental and behavioral disorders ∗ 21 0.67

The number of Jamu classified into multiple disease classes 119 3.79The number of Jamu unclassified 4 0.13Total Jamu formulas 3138 100.00

where 𝑥𝑖 is the weight of plant-𝑖 in Jamu 𝑋, 𝑦𝑖 is the weightof plant-𝑖 in Jamu 𝑌, 𝑥 is mean of Jamu 𝑋, and 𝑦 is meanof Jamu 𝑌. The higher similarity between Jamu pairs thehigher the correlation value. In the present study, 𝑥𝑖 and𝑦𝑖 are assigned as 1 or 0 in cases the 𝑖th plant is, respec-tively, included or not included in the formula. Under suchcondition, Pearson correlation corresponds to fourfold pointcorrelation coefficient; that is,

corr (𝑋, 𝑌) = 𝑎𝑑 − 𝑏𝑐

√(𝑎 + 𝑏) (𝑎 + 𝑐) (𝑏 + 𝑑) (𝑐 + 𝑑), (2)

where 𝑎, 𝑏, 𝑐, and 𝑑 represent the numbers of plants includedin both 𝑋 and 𝑌, in only 𝑋, in only 𝑌, and in neither 𝑋 nor𝑌, respectively.

In step 2 the Jamu clusters are generated using net-work clustering algorithm DPClusO. DPClusO can generateclusters characterized by high density and identified byperiphery; that is, the Jamu medicines belonging to a clusterare highly cohesive and separated by a natural boundary. Suchclusters contain potential information about plant-diseaserelations.

In step 3 we assess disease-dominant clusters based onmatching 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 numberof Jamu associated with a single disease to the total numberof Jamu in the cluster. We assign a disease to a cluster forwhich the matching score is greater than a threshold value.In step 4, we determine the frequency of plants associatedwith a cluster if and only if a disease is assigned to it in theprevious step. The highest frequency plant associated to acluster is considered to be related to the disease assigned tothat cluster. True positive rates (TPR) or sensitivity was usedto evaluate resulting plants. TPR is the proportion of the truepositive predictions out of all the true predictions, defined bythe following formula [18]:

TPR = TPTP + FN

, (4)

where true positive (TP) is the number of correctly classifiedand false negative (FN) is the number of incorrectly rejectedentities. We refer to the proposed method as supervisedclustering because after generation of the clusters we narrowdown the candidate clusters for further analysis based onsupervised learning and thus improve the accuracy of predic-tion of the proposed method.

3. Result and Discussion

3.1. Construction and Comparison of Jamu and RandomNetworks. We used the same number of Jamu formulas fromprevious research [6], 3138 Jamu formulas, and the set union


A BC D

DCBA

Step 1

Constructing ingredient correlation network

Step 2

Extracting highly connected Jamu

Step 3

Supervised analysis for voting utilization

Step 4

Listing ingredients

Input: Jamu formulas

Output: plant-disease relations

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

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


Table 3: Statistics of three datasets.

Parameters 0.7% 0.5% 0.3%

Networkstatistics

Total pairs 34,454 24,610 14,766Minimum correlation 0.596 0.665 0.718Number of Jamu formulas 2,779 2,496 2,085Average degree 24.8 19.7 14.2(Random network: ER) (24.8 ± 0.0) (19.7 ± 0.0) (14.2 ± 0.0)

(Random network: BA) (24.7 ± 0.1) (19.7 ± 0.1) (14.1 ± 0.1)

(Random network: CNN) (24.7 ± 0.4) (19.7 ± 0.4) (14.0 ± 0.4)

Clustering coefficient 0.521 0.520 0.540(Random network: ER) (0.009 ± 0.000) (0.008 ± 0.000) (0.007 ± 0.000)



Number of connected components 69 119 254(Random networks: ER, BA, CNN) (1) (1) (1)Network diameter 15 17 20(Random network: ER) (4.0 ± 0.0) (4.0 ± 0.0) (5.0 ± 0.0)



Network density 0.008 0.008 0.007(Random network: ER) (0.009 ± 0.000) (0.008 ± 0.000) (0.007 ± 0.000)



DPClusO

Total number of clusters 1,746 1,411 938Number of clusters with more than 2 Jamu 1,296 873 453(%) (74.2) (61.9) (48.3)Number of Jamu formulas in the biggest cluster 118 104 89

of all formulas consists of 465 plants. We assigned 3138 Jamuformulas to 116 diseases of International Classification ofDiseases (ICD) version 10 from World Health Organization(WHO, Table 1) [19]. Those 116 diseases are mapped to18 classes of disease, which contains 16 classes of diseasefromNational Center for Biotechnology Information (NCBI)[20] and 2 additional classes. Table 2 shows distributionof 3138 Jamu into 18 classes of disease. According to thisclassification, most Jamu formulas are useful for relievingmuscle and bone, nutritional and metabolic diseases, andthe digestive system. Furthermore, there is no Jamu formulaclassified into glands and hormones and neonatal diseaseclasses. We excluded 4 Jamu formulas which are used to treatfever in the evaluation process because this symptom is verygeneral and almost appeared in all disease classes. Jamu-plant-disease relations can be represented using 2 matrices:first matrix is Jamu-plant relation with dimension 3138 ×465 and the second matrix is Jamu-disease relation withdimension 3138 × 18.

After completion of data acquisition process, we calcu-lated the similarity between Jamu pairs using correlationmeasure. The similarity measures between Jamu pairs weredetermined based on their ingredients. Corresponding to 𝐾(3138 in present case) Jamu formulas, there can be maximum(𝐾 × (𝐾 − 1)/2) = (3138 × (3137/2)) = 4,921,953 Jamu

pairs. We sorted the Jamu pairs based on correlation valueusing descending order and selected top-𝑛 (0.7%, 0.5%,and 0.3%) pairs of Jamu formula to create 3 sets of Jamupairs. The number of Jamu pairs for 0.7%, 0.5%, and 0.3%datasets is 34,454 pairs, 24,610 pairs, and 14,766 pairs andthe corresponding minimum correlation values are 0.596,0.665, and 0.718, respectively. The three datasets of Jamupairs can be regarded as three undirected networks (step 1 inFigure 1) consisting of 2779, 2496, and 2085 Jamu formulas,respectively (Table 3). Figure 2 shows visualization of 0.7%Jamunetworks usingCytoscape Spring Embedded layout.Weverified that the degree distributions of the Jamu networksare somehow close to those of scale-free networks, that is,roughly are of power law type. However, in the high-degreeregion the power law structure is broken (Figure 3). Nearlyaccurate relation of power laws between medicinal herbsand the number of formulas utilizing them was observed inJamu system but not in Kampo (Japanese crude drug system)[4]. The difference of formulas between Jamu and Kampocan be explained by herb selection by medicinal researchersbased on the optimization process of selection [4]. Thus,the broken structure of power law corresponding to Jamunetworks is associated with the fact that selection of Jamupairs based on ingredient correlation leads to nonrandomselection. We also constructed random networks according


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to Erdos-Renyi (ER) model [21], Barabasi-Albert (BA) model[22], and Vazquez’s Connecting Nearest Neighbor (CNN)model [23] of the same size corresponding to each of the realJamu network. We used Cytoscape Network Analyzer plugin[24] and R software for analyzing the characteristics of boththe Jamu and the random networks.

We determined five statistical indexes, that is, averagedegree, clustering coefficient, number of connected compo-nent, network diameter, and network density of each Jamunetwork and also of each random network. The clusteringcoefficient 𝐶𝑛 of a node 𝑛 is defined as 𝐶𝑛 = 2𝑒𝑛/(𝑘𝑛(𝑘𝑛 − 1)),where 𝑘𝑛 is the number of neighbors of 𝑛 and 𝑒𝑛 is the numberof connected pairs between all neighbors of 𝑛. The networkdiameter is the largest distance between any two nodes. If

a network is disconnected, its diameter is themaximum of alldiameters of its connected components. A network’s densityis the ratio of the number of edges in the network over thetotal number of possible edges between all pairs of nodes(which is 𝑛(𝑛 − 1)/2, where 𝑛 is the number of vertices, foran undirected graph). The average number of neighbors andthe network density are the same for the real and randomnetworks of the same size as it is shown in Table 3. In caseof 0.7% and 0.5% real networks, the clustering coefficient isroughly the same and in case of 0.3% the clustering coefficientis somewhat larger. The number of connected componentsand the diameter of the Jamu networks gradually decreaseas the network grows bigger by addition of more nodes andedges.


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Very different values corresponding to clustering coef-ficient, connected component, and network diameter implythat the Jamu networks are quite different from all 3 typesof random networks.The differences between Jamu networksand ER random networks are the largest. Random networksconstructed based on other two models are also substantiallydifferent from Jamu networks. Based on the fact that therandom networks constructed based on all three types ofmodels are different from the Jamu networks, it can beconcluded that structure of Jamu networks is reasonablybiased and thus might contain certain information about

plant-disease relations. Specially, much higher value corre-sponding to clustering coefficient indicates that there areclusters in the networks worthy to be investigated. To extractclusters from the Jamu networks (step 2 in Figure 1) weapplied DPClusO network clustering algorithm [14] to gen-erate overlapping clusters based on density and peripherytracking.

3.2. Supervised Clustering Based on DPClusO. DPClusO is ageneral-purpose clustering algorithm and useful for findingoverlapping cohesive groups in an undirected simple graph


Table 4: List of plants assigned to each disease.

Number Plants name Hit-miss statusA. Disease: blood and lymph diseases

1 Tamarindus indica Hit ∗

2 Allium sativum Hit ∗

3 Tinospora tuberculata Hit ∗

4 Piper retrofractum Hit5 Syzygium aromaticum Hit ∗

6 Bupleurum falcatum Hit7 Graptophyllum pictum Hit8 Plantago major Hit9 Zingiber officinale Hit ∗

10 Cinnamomum burmannii Hit ∗

11 Soya max Miss ∗12 Kaempferia galanga Hit13 Curcuma longa Hit ∗

14 Piper nigrum Hit15 Zingiber aromaticum Hit ∗

16 Phyllanthus urinaria Hit ∗

17 Oryza sativa Hit18 Myristica fragrans Hit ∗

19 Alstonia scholaris Hit ∗

20 Syzygium polyanthum Miss21 Andrographis paniculata Hit ∗

22 Sida rhombifolia Miss23 Cyperus rotundus Hit24 Sonchus arvensis Miss25 Curcuma aeruginosa Hit ∗

26 Curcuma xanthorrhiza HitB. Disease: cancers

1 Catharanthus roseus HitC. Disease: the digestive system

1 Foeniculum vulgare Hit2 Glycyrrhiza uralensis Hit ∗

3 Imperata cylindrica Hit4 Zingiber purpureum Hit ∗

5 Physalis peruviana Hit6 Punica granatum Hit ∗

7 Echinacea purpurea Hit8 Zingiber officinale Hit ∗

9 Psidium guajava Hit10 Baeckea frutescens Hit ∗

11 Amomum compactum Hit12 Cinnamomum burmannii Hit ∗

13 Melaleuca leucadendra Hit14 Caesalpinia sappan Hit ∗

15 Parkia roxburghii Hit16 Rheum tanguticum Hit17 Kaempferia galanga Hit18 Coriandrum sativum Hit

Table 4: Continued.

Number Plants name Hit-miss status19 Curcuma longa Hit20 Zingiber aromaticum Hit21 Phyllanthus urinaria Hit22 Myristica fragrans Hit23 Hydrocotyle asiatica Hit ∗

24 Carica papaya Hit25 Mentha arvensis Hit26 Lepiniopsis ternatensis Hit27 Helicteres isora Hit28 Andrographis paniculata Hit29 Symplocos odoratissima Hit30 Schisandra chinensis Hit31 Blumea balsamifera Hit32 Silybum marianum Hit ∗

33 Cinnamomum sintoc Hit34 Elephantopus scaber Hit35 Curcuma aeruginosa Hit36 Kaempferia pandurata Hit37 Curcuma xanthorrhiza Hit38 Curcuma mangga Hit ∗

39 Curcuma zedoaria Hit40 Daucus carota Hit ∗

41 Matricaria chamomilla Hit ∗

42 Cymbopogon nardus Hit ∗

D. Disease: female-specific diseases1 Foeniculum vulgare Hit2 Imperata cylindrica Hit3 Tamarindus indica Hit4 Pluchea indica Hit ∗

5 Piper retrofractum Hit6 Punica granatum Hit7 Uncaria rhynchophylla Hit8 Zingiber officinale Hit9 Guazuma ulmifolia Hit ∗

10 Nigella sativa Hit11 Terminalia bellirica Hit12 Baeckea frutescens Hit13 Phaseolus radiatus Hit14 Amomum compactum Hit ∗

15 Sauropus androgynus Hit16 Usnea misaminensis Hit17 Cinnamomum burmannii Hit18 Melaleuca leucadendra Hit19 Parameria laevigata Hit20 Parkia roxburghii Hit21 Piper cubeba Hit22 Kaempferia galanga Hit


Table 4: Continued.

Number Plants name Hit-miss status23 Coriandrum sativum Hit24 Kaempferia angustifolia Hit25 Curcuma longa Hit26 Zingiber aromaticum Hit27 Languas galanga Hit28 Galla lusitania Hit29 Quercus lusitanica Hit30 Hydrocotyle asiatica Hit31 Areca catechu Hit32 Lepiniopsis ternatensis Hit33 Helicteres isora Hit ∗

34 Piper betle Hit35 Elephantopus scaber Hit ∗

36 Kaempferia pandurata Hit37 Curcuma xanthorrhiza Hit38 Sesbania grandiflora Hit

E. Disease: the heart and blood vessels1 Allium sativum Hit2 Curcuma longa Hit ∗

3 Morinda citrifolia Hit ∗

4 Homalomena occulta Hit ∗

5 Hydrocotyle asiatica Hit6 Alstonia scholaris Hit ∗

7 Syzygium polyanthum Miss ∗8 Andrographis paniculata Hit ∗

9 Apium graveolens Miss10 Imperata cylindrica Hit

F. Disease: male-specific diseases1 Cucurbita pepo Miss2 Serenoa repens Miss3 Baeckea frutescens Hit4 Phaseolus radiatus Hit5 Curcuma longa Hit6 Elephantopus scaber Hit

G. Disease: muscle and bone1 Foeniculum vulgare Hit2 Clausena anisum-olens Hit ∗

3 Zingiber purpureum Hit4 Allium sativum Hit5 Strychnos ligustrina Hit6 Tinospora tuberculata Hit ∗

7 Piper retrofractum Hit8 Syzygium aromaticum Hit9 Cola nitida Hit ∗

10 Ginkgo biloba Hit ∗

11 Panax ginseng Hit12 Equisetum debile Hit ∗

13 Zingiber officinale Hit

Table 4: Continued.

Number Plants name Hit-miss status14 Ganoderma lucidum Hit15 Nigella sativa Hit16 Terminalia bellirica Hit ∗

17 Baeckea frutescens Hit ∗

18 Amomum compactum Hit19 Cinnamomum burmannii Hit20 Melaleuca leucadendra Hit21 Parameria laevigata Hit ∗

22 Psophocarpus tetragonolobus Hit ∗

23 Parkia roxburghii Hit24 Piper cubeba Hit ∗

25 Kaempferia galanga Hit26 Coriandrum sativum Hit27 Cola acuminata Hit28 Coffea arabica Hit29 Orthosiphon stamineus Hit30 Curcuma longa Hit31 Piper nigrum Hit32 Alpinia galanga Hit33 Vitex trifolia Hit34 Zingiber amaricans Hit ∗

35 Zingiber zerumbet Hit36 Zingiber aromaticum Hit37 Languas galanga Hit38 Massoia aromatica Hit39 Morinda citrifolia Hit40 Carum copticum Hit ∗

41 Panax pseudoginseng Hit ∗

42 Oryza sativa Hit43 Myristica fragrans Hit44 Pandanus amaryllifolius Hit45 Eurycoma longifolia Hit46 Hydrocotyle asiatica Hit47 Areca catechu Hit ∗

48 Mentha arvensis Hit ∗

49 Lepiniopsis ternatensis Hit50 Pimpinella pruatjan Hit51 Andrographis paniculata Hit52 Blumea balsamifera Hit53 Cymbopogon nardus Hit54 Sida rhombifolia Hit55 Cinnamomum sintoc Hit56 Piper betle Hit ∗

57 Talinum paniculatum Hit58 Elephantopus scaber Hit59 Cyperus rotundus Hit60 Curcuma aeruginosa Hit61 Kaempferia pandurata Hit ∗


Table 4: Continued.

Number Plants name Hit-miss status62 Curcuma xanthorrhiza Hit63 Tribulus terrestris Hit64 Corydalis yanhusuo Hit65 Pausinystalia yohimbe HitH. Disease: nutritional andmetabolic diseases1 Foeniculum vulgare Hit2 Glycyrrhiza uralensis Hit3 Zingiber purpureum Hit4 Allium sativum Hit5 Tinospora tuberculata Hit6 Pandanus conoideus Hit7 Syzygium aromaticum Hit8 Punica granatum Hit9 Zingiber officinale Hit10 Guazuma ulmifolia Hit11 Nigella sativa Hit12 Amomum compactum Hit ∗13 Cinnamomum burmannii Hit14 Parameria laevigata Hit15 Caesalpinia sappan Hit16 Soya max Hit ∗17 Cocos nucifera Hit18 Rheum tanguticum Hit19 Piper cubeba Hit ∗20 Murraya paniculata Hit21 Kaempferia galanga Hit ∗22 Coffea arabica Hit ∗23 Orthosiphon stamineus Hit24 Curcuma longa Hit25 Piper nigrum Hit ∗26 Zingiber aromaticum Hit27 Aloe vera Hit28 Phaleria papuana Hit29 Galla lusitania Hit30 Quercus lusitanica Hit31 Morinda citrifolia Hit32 Myristica fragrans Hit ∗33 Momordica charantia Hit34 Areca catechu Hit35 Lepiniopsis ternatensis Hit36 Alstonia scholaris Hit37 Hibiscus sabdariffa Hit38 Laminaria japonica Hit39 Syzygium polyanthum Hit40 Andrographis paniculata Hit41 Sindora sumatrana Hit ∗42 Cassia angustifolia Hit43 Woodfordia floribunda Hit

Table 4: Continued.

Number Plants name Hit-miss status44 Piper betle Hit45 Spirulina Hit46 Stevia rebaudiana Hit47 Theae sinensis Hit48 Sonchus arvensis Hit49 Curcuma heyneana Hit50 Curcuma aeruginosa Hit51 Kaempferia pandurata Hit ∗

52 Curcuma xanthorrhiza Hit53 Curcuma zedoaria Hit ∗

54 Olea europaea HitI. Disease respiratory diseases

1 Foeniculum vulgare Hit2 Clausena anisum-olens Hit3 Glycyrrhiza uralensis Hit4 Zingiber purpureum Hit5 Piper retrofractum Hit ∗

6 Syzygium aromaticum Hit7 Gaultheria punctata Hit8 Panax ginseng Hit9 Equisetum debile Hit ∗

10 Zingiber officinale Hit11 Citrus aurantium Hit ∗

12 Nigella sativa Hit ∗

13 Amomum compactum Hit14 Cinnamomum burmannii Hit15 Melaleuca leucadendra Hit16 Parkia roxburghii Hit17 Cocos nucifera Hit18 Piper cubeba Hit19 Kaempferia galanga Hit20 Coriandrum sativum Hit21 Curcuma longa Hit22 Piper nigrum Hit23 Zingiber aromaticum Hit24 Languas galanga Hit25 Mentha piperita Hit26 Oryza sativa Hit ∗

27 Myristica fragrans Hit28 Pandanus amaryllifolius Hit ∗

29 Hydrocotyle asiatica Hit ∗

30 Mentha arvensis Hit31 Lepiniopsis ternatensis Hit32 Helicteres isora Hit33 Blumea balsamifera Hit34 Cymbopogon nardus Hit35 Piper betle Hit36 Curcuma xanthorrhiza Hit


Table 4: Continued.

Number Plants name Hit-miss status37 Salix alba Hit ∗

38 Matricaria chamomilla Miss ∗J. Disease: skin and connective tissue

1 Strychnos ligustrina Hit2 Merremia mammosa Hit ∗

3 Piper retrofractum Hit ∗

4 Santalum album Hit5 Zingiber officinale Hit ∗

6 Citrus aurantium Hit7 Citrus hystrix Hit8 Cassia siamea Hit9 Cocos nucifera Hit10 Trigonella foenum-graecum Hit11 Orthosiphon stamineus Hit12 Curcuma longa Hit13 Vetiveria zizanioides Hit14 Aloe vera Hit15 Rosa chinensis Hit16 Jasminum sambac Hit17 Phyllanthus urinaria Hit18 Mentha piperita Hit19 Oryza sativa Hit20 Myristica fragrans Hit ∗

21 Hydrocotyle asiatica Hit22 Lepiniopsis ternatensis Hit23 Alstonia scholaris Hit24 Andrographis paniculata Hit25 Cymbopogon nardus Hit26 Piper betle Hit27 Theae sinensis Hit28 Curcuma heyneana Hit29 Kaempferia pandurata Hit ∗

30 Curcuma xanthorrhiza Hit31 Melaleuca leucadendra Hit32 Matricaria chamomilla Miss ∗

K. Disease: the urinary system1 Foeniculum vulgare Hit ∗

2 Imperata cylindrica Hit ∗

3 Strychnos ligustrina Hit ∗

4 Plantago major Hit5 Zingiber officinale Hit ∗

6 Cinnamomum burmannii Hit ∗

7 Strobilanthes crispus Hit8 Kaempferia galanga Hit ∗

9 Orthosiphon stamineus Hit10 Phyllanthus urinaria Hit11 Blumea balsamifera Hit ∗

12 Sonchus arvensis Hit13 Curcuma xanthorrhiza Hit∗indicates that plant will not assigned if we use matching score >0.7.

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for any type of application. It ensures coverage and performsrobustly in case of random addition, removal, and rearrange-ment of edges in protein-protein interaction (PPI) networks[14]. While applying DPClusO, the parameter values ofdensity and cluster property that we used in this experimentare 0.9 and 0.5, respectively [15]. Table 3 shows the summaryof clustering result by DPClusO. Because clusters consistingof two Jamu formulas are trivial clusters, for the next stepswe only use clusters each of which consists of 3 or moreJamu formulas. The number of total clusters increases alongwith the larger dataset, although the threshold correlationbetween Jamu pairs decreases. We evaluated the clusteringresult using matching score to determine dominant diseasefor every cluster (step 3 in Figure 1). Matching score of acluster is the ratio of the highest number of Jamu associatedwith the same disease to the total number of Jamu in thecluster. Thus matching score is a measure to indicate howstrongly a disease is associated to a cluster. Figure 4 showsthe distribution of the clusters with respect to matching scorefrom three datasets. All datasets have the highest frequencyof clusters at matching score >0.9 and overall most of theclusters have higher matching score, which means most ofthe DPClusO generated clusters can be confidently relatedto a dominant disease. Furthermore the number of clusterswith matching score >0.9 is remarkably larger compared tothe same in other ranges ofmatching score in case of the 0.3%dataset (Figure 4(c)). If we compare the ratio of frequency ofclusters at matching score >0.9 for every dataset, the 0.3%dataset has the highest ratio with 40.84% (of 453), comparedto 29.67% (of 873) and 21.91% (of 1296), in case of 0.5% and0.7% datasets, respectively. Thus, the most reliable speciesto disease relations can be predicted at matching score >0.9corresponding to the clusters generated from 0.3% dataset.

Figure 5(a) shows the success rate for all 3 datasets withrespect to threshold matching scores. Success rate is definedas the ratio of the number of clusters with matching scorelarger than the threshold to the total number of clusters.As expected it tends to produce lower success rate if wedecrease correlation value to create the datasets. Howevermore clusters are generated and more information can beextracted when we lower the threshold correlation value.Thesuccess rate increases rapidly as the matching score decreases


Table 5: Relation between disease classes in NCBI and efficacy classes reported by Afendi et al. [6].

Class of disease Ref. Efficacy classD1 Blood and lymph diseases NCBI E7 Pain/inflammation (PIN)D2 Cancers NCBI E7 Pain/inflammation (PIN)

D3The digestive system NCBI E4 Gastrointestinal disorders (GST)E7 Pain/inflammation (PIN)

D4 Ear, nose, and throat NCBI E7 Pain/inflammation (PIN)D5 Diseases of the eye NCBI E7 Pain/inflammation (PIN)D6 Female-specific diseases NCBI E5 Female reproductive organ problems (FML)D7 Glands and hormones NCBI E7 Pain/inflammation (PIN)D8The heart and blood vessels NCBI E7 Pain/inflammation (PIN)D9 Diseases of the immune system NCBI E7 Pain/inflammation (PIN)D10Male-specific diseases NCBI E6Musculoskeletal and connective tissue disorders (MSC)D11Muscle and bone NCBI E6Musculoskeletal and connective tissue disorders (MSC)D12 Neonatal diseases NCBI E7 Pain/inflammation (PIN)D13The nervous system NCBI E7 Pain/inflammation (PIN)

D14 Nutritional and metabolic diseases NCBI E2 Disorders of appetite (DOA)E4 Gastrointestinal disorders (GST)

D15 Respiratory diseases NCBI E8 Respiratory disease (RSP)E7 Pain/inflammation (PIN)

D16 Skin and connective tissue NCBI E9Wounds and skin infections (WND)D17The urinary system ∗ E1 Urinary related problems (URI)D18Mental and behavioural disorders ∗ E3 Disorders of mood and behavior (DMB)

from 0.9 to 0.6 and after that the slope of increase of successrate decreases. Therefore in this study we empirically decide0.6 as the threshold matching score to predict plant-diseaserelations.

3.3. Assignment of Plants to Disease. By using DPClusO re-sulting clusters, we assigned plants to classes of disease. Basedon a threshold matching score we assigned dominant diseaseto a cluster. Then we assign a plant to a cluster by way ofanalyzing the ingredients of the Jamu formulas belongingto that cluster and determining the highest frequency plant,that is, the plant that is used for maximum number Jamubelonging to that cluster (step 4 in Figure 1). Thus we assigna disease and a plant to each cluster having matching scoregreater than a threshold. Our hypothesis is that the diseaseand the plant assigned to the same cluster are related.

The total number of assigned plants depends onmatchingscore value. Figure 5(b) shows the number of predicted plantsthat can be assigned to diseases in the context of matchingscore. With higher matching score value, the number ofpredicted plants assigned to classes of disease is supposed toremain similar or decrease but the reliability of predictionincreases. In Figure 5(b) a sudden change in the numberof predicted plants is seen at matching score 0.6 which weconsider as empirical threshold in this work. Based on the0.7%dataset, the largest number of plants (135 plants, Table 4)was assigned to diseases. There are 63 plants assigned to onlyone class of disease, whereas the other 72 plants are assignedto at least two or more classes of disease (Figure 6).

3.4. Evaluation of the Supervised Clustering Based on DPClu-sO. Weused previously published results [6] as gold standardto evaluate our results. The previous study assigned plantsto 9 kinds of efficacy whereas we assigned the plants to 18disease classes (16 from NCBI and 2 additional classes). Forthe sake of evaluation we got done amapping of the 18 diseaseclasses to 9 efficacy classes by a professional doctor, whichis shown in Table 5. Table 6 shows the prediction result ofplant-disease relations for all 3 datasets, corresponding toclusters with matching score greater than 0.6. Table 6 alsoshows corresponding efficacy, the number of assigned plants,number of correctly predicted plants, and true positive rates(TPR), respectively.

We determined TPR corresponding to a disease/efficacyclass by calculating the ratio of the number of correctprediction to the number of all predictions. When a diseasecorresponds to more than one kind of efficacy, the highestTPR can be considered the TPR for the correspondingdisease. For all 3 datasets the TPR corresponding to eachdisease is roughly 90% or more. The 0.3% dataset consists ofJamu pairs with higher correlation values and based on thisdataset 117 plants are assigned to 14 disease classes. The 0.7%dataset contains more Jamu pairs and assigned plants to11 disease classes, one less disease class compared to 0.5%dataset. The two disease classes covered by 0.3% datasetbut not covered by 0.5% and 0.7% datasets are the nervoussystem (D13) and disease of the immune system (D9). Theonly disease class covered by 0.3% and 0.5% datasets butnot covered by 0.7% dataset is mental and behaviouraldisorders (D18). The larger dataset network tends to have


Table 6: The prediction result of plant-disease relations using matching score >0.6.

Class ofdisease

Correspondingefficacy

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Correctprediction

Truepositiverate

Number ofassignedplants

Correctprediction

Truepositiverate

Number ofassignedplants

Correctprediction

Truepositiverate

D1 E7 26 22 0.85 24 20 0.83 24 20 0.83D2 E7 1 1 1.00 5 5 1.00 1 1 1.00

D3 E4 42 42 1.00 33 33 1.00 28 28 1.00E7 38 0.90 30 0.91 25 0.89

D4 E7 0 0 — 0 0 — 0 0 —D5 E7 0 0 — 0 0 — 0 0 —D6 E5 38 38 1.00 37 37 1.00 32 32 1.00D7 E7 0 0 — 0 0 — 0 0 —D8 E7 10 8 0.80 8 7 0.88 6 5 0.83D9 E7 0 0 — 0 0 — 1 1 1.00D10 E6 6 4 0.67 2 0 — 3 1 0.33D11 E6 65 65 1.00 71 71 1.00 60 60 1.00D12 E7 0 0 — 0 0 — 0 0 —D13 E7 0 0 — 0 0 — 5 5 1.00

D14 E2 54 44 0.81 45 36 0.80 35 26 0.74E4 54 1.00 45 1.00 35 1.00

D15 E7 38 37 0.97 34 34 1.00 33 33 1.00E8 31 0.82 30 0.88 29 0.88

D16 E9 32 31 0.97 32 32 1.00 27 27 1.00D17 E1 13 13 1.00 9 9 1.00 8 8 1.00D18 E3 0 0 — 5 5 1.00 4 4 1.00

Total assigned plants 135 129 117

lower coverage of disease classes. The number of Jamu pairs,that is, the number of edges in the network, affect the numberof DPClusO resulting clusters and number of Jamu formulasper cluster. As a consequence, for the larger dataset networks,the success rate becomes lower and the coverage of diseaseclasses is lower but prediction of more plant-disease relationscan be achieved.

4. Conclusions

This paper introduces a novel method called supervisedclustering for analyzing big biological data by integrat-ing network clustering and selection of clusters based onsupervised learning. In the present work we applied themethod for data mining of Jamu formulas accumulatedin KNApSAcK database. Jamu networks were constructedbased on correlation similarities between Jamu formulas andthen network clustering algorithm DPClusO was applied togenerate high density Jamu modules. For the analysis ofthe next steps potential clusters were selected by supervisedlearning. The successful clusters containing several Jamurelated to the same disease might be useful for finding mainingredient plant for that disease and the lower matchingscore value clusters will be associated with varying plants

which might be supporting ingredients. By applying theproposed method important plants from Jamu formulas forevery classes of disease were determined.The plant to diseaserelations predicted by proposed network based method wereevaluated in the context of previously published results andwere found to produce a TPR of 90%. For the larger datasetnetworks, success rate and the coverage of disease classesbecome lower but prediction of more plant-disease relationscan be achieved.

Conflict of Interests

The authors declare that there is no financial interest orconflict of interests regarding the publication of this paper.

Acknowledgments

Thisworkwas supported by theNational BioscienceDatabaseCenter in Japan and the Ministry of Education, Culture,Sports, Science, and Technology of Japan (Grant-in-Aidfor Scientific Research on Innovation Areas “BiosyntheticMachinery. Deciphering and Regulating the System for Cre-ating StructuralDiversity of BioactivityMetabolites (2007)”).


References

[1] R. Verporte, H. K. Kim, and Y. H. Choi, “Plants as source ofmedicines,” inMedicinal and Aromatic Plants, R. J. Boger, L. E.Craker, and D. Lange, Eds., chapter 19, pp. 261–273, 2006.

[2] A. Furnharm, “Why do people choose and use complemen-tary therapies?” in Complementary Medicine: An ObjectiveAppraisal, E. Ernst, Ed., pp. 71–88, Butterworth-Heinemann,Oxford, UK, 1996.

[3] E. Ernst, “Herbal medicines put into context,” British MedicalJournal, vol. 327, no. 7420, pp. 881–882, 2003.

[4] F. M. Afendi, T. Okada, M. Yamazaki et al., “KNApSAcK familydatabases: integrated metabolite—plant species databases formultifaceted plant research,” Plant and Cell Physiology, vol. 53,no. 2, p. e1, 2012.

[5] F.M.Afendi, N.Ono, Y.Nakamura et al., “Dataminingmethodsfor omics and knowledge of crude medicinal plants towardbig data biology,” Computational and Structural BiotechnologyJournal, vol. 4, no. 5, Article ID e201301010, 2013.

[6] F. M. Afendi, L. K. Darusman, A. Hirai et al., “System biologyapproach for elucidating the relationship between Indonesianherbal plants and the efficacy of Jamu,” in Proceedings of the10th IEEE International Conference on Data Mining Workshops(ICDMW ’10), pp. 661–668, Sydney, Australia, December 2010.

[7] F. M. Afendi, L. K. Darusman, A. H. Morita et al., “Efficacy ofJamu formulations by PLS modeling,” Current Computer-AidedDrug Design, vol. 9, pp. 46–59, 2013.

[8] F. M. Afendi, L. K. Darusman, M. Fukuyama, M. Altaf-Ul-Amin, and S. Kanaya, “A bootstrapping approach for investi-gating the consistency of assignment of plants to Jamu efficacyby PLS-DAmodel,”Malaysian Journal ofMathematical Sciences,vol. 6, no. 2, pp. 147–164, 2012.

[9] W. Winterbach, P. V. Mieghem, M. Reinders, H. Wang, and D.de Ridder, “Topology of molecular interaction networks,” BMCSystems Biology, vol. 7, article 90, 2013.

[10] C. Bachmaier, U. Brandes, and F. Schreiber, “Biological net-work,” in Handbook of Graph Drawing and Visualization, pp.621–651, CRC Press, 2013.

[11] X. Chen, M. Chen, and K. Ning, “BNArray: an R package forconstructing gene regulatory networks from microarray databy using Bayesian network,” Bioinformatics, vol. 22, no. 23, pp.2952–2954, 2006.

[12] P. Langfelder and S. Horvath, “WGCNA: an R package forweighted correlation network analysis,” BMC Bioinformatics,vol. 9, article 559, 2008.

[13] A. Martin, M. E. Ochagavia, L. C. Rabasa, J. Miranda, J.Fernandez-de-Cossio, and R. Bringas, “BisoGenet: a new toolfor gene network building, visualization and analysis,” BMCBioinformatics, vol. 11, article 91, 2010.

[14] M. Altaf-Ul-Amin, M. Wada, and S. Kanaya, “Partitioning aPPI network into overlapping modules constrained by high-density and periphery tracking,” ISRN Biomathematics, vol.2012, Article ID 726429, 11 pages, 2012.

[15] M. Altaf-Ul-Amin, H. Tsuji, K. Kurokawa, H. Asahi, Y. Shinbo,and S. Kanaya, “DPClus: a density-periphery based graphclustering software mainly focused on detection of proteincomplexes in interaction networks,” Journal of Computer AidedChemistry, vol. 7, pp. 150–156, 2006.

[16] S. K. Kachigan, Multivariate Statistical Analysis: A ConceptualIntroduction, Radius Press, New York, NY, USA, 1991.

[17] J. L. Rodgers and W. A. Nicewander, “Thirteen ways to look atthe correlations coefficient,”TheAmerican Statiscian, vol. 42, pp.59–66, 1995.

[18] M. Li, J.-E. Chen, J.-X. Wang, B. Hu, and G. Chen, “Modifyingthe DPClus algorithm for identifying protein complexes basedon new topological structures,” BMC Bioinformatics, vol. 9,article 398, 2008.

[19] World Health Organization, “International Classification ofDiseases (ICD) 10,” 2010, http://www.who.int/classifications/icd/en/.

[20] National Center for Biotechnology Information, Genes andDisease, NCBI, Bethesda, Md, USA, 1998.

[21] P. Erdos and A. Renyi, “On the evolution of random graph,”Publicationes Mathematicae Debrecen, vol. 6, pp. 290–297, 1959.

[22] A.-L. Barabasi and R. Albert, “Emergence of scaling in randomnetworks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.

[23] A. Vazquez, “Growing network with local rules: preferentialattachment, clustering hierarchy, anddegree correlations,”Phys-ical Review E—Statistical, Nonlinear, and Soft Matter Physics,vol. 67, no. 5, Article ID 056104, 15 pages, 2003.

[24] Max Planck Institut Informatik, “NetworkAnalyzer,” 2013,http://med.bioinf.mpi-inf.mpg.de/netanalyzer/index.php.

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