Supervised Clustering Based on DPClusO: Prediction of Plant

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; [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 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 scientific 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 Classification of Diseases (ver. 10) which belong to 18 classes of disease from National Center for Biotechnology Information. The 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. The 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 fields such as genomics, transcriptomics, proteomics, and metabolomics as well as in several other fields such as ethnomedicinal survey. The 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]. These medicines consist of ingredients made from plants, animals, minerals, or combination of them. The 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 effects on human physiology through a variety of biological pathways. The KNApSAcK Family database systems can be used to comprehensively understand the medicinal usage of plants based upon traditional and modern knowledge [4, 5]. This

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Table 1: List of diseases using International Classification 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, inflammation Colic abdomen, bloating (in infant) Common cold Common cold, dyspepsia, insect bites Common cold, influenza 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 Immunodefficiency 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

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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 scientific analyses are needed to support their efficacy and their safety. Attaining this objective is in accordance with the 2010 policy of the Ministry of Health of Indonesian Government about scientification of Jamu. Thus, it is required to systemize the formulations and develop basic scientific principles of Jamu to meet the requirement of Indonesian Healthcare System. Afendi et al. initiated and conducted scientific analysis of Jamu for finding the correlation between plants, Jamu, and their efficacy using statistical methods [6–8]. They used Biplot, partial least squares (PLS), and bootstrapping methods to summarize the data and also focused on prediction of Jamu formulations. These methods give a good understanding about relationship between plants, Jamu, and their efficacy. Among 465 plants used in 3138 Jamu, 190 plants were shown to be effective for at least one efficacy and these plants were considered

to be the main ingredients of Jamu. The other 275 plants are considered to be supporting ingredients in Jamu because their efficacy has not been established yet. Network biology can be defined 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]. This 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. These 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 efficacy by global linear modeling of the Jamu ingredients and efficacy. 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 effectiveness of a plant against a disease is firmly established, then further analysis about that plant can be proceeded to molecular level to pinpoint the drug targets. The 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. The 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. The similarity is represented by Pearson correlation coefficient [16, 17]; that is,

corr (𝑋, 𝑌) =

∑𝑙𝑖=1 (𝑥𝑖 − 𝑥) (𝑦𝑖 − 𝑦) √∑𝑙 (𝑥𝑖 − 𝑥)2 ∑𝑙 (𝑦𝑖 − 𝑦)2 𝑖=1 𝑖=1

,

(1)

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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 The digestive system Ear, nose, and throat Diseases of the eye Female-specific diseases Glands and hormones The heart and blood vessels Diseases of the immune system Male-specific diseases Muscle and bone Neonatal diseases The nervous system Nutritional and metabolic diseases Respiratory diseases Skin and connective tissue The 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

The number of Jamu classified into multiple disease classes The number of Jamu unclassified

119 4

3.79 0.13

Total Jamu formulas

3138

100.00

where 𝑥𝑖 is the weight of plant-𝑖 in Jamu 𝑋, 𝑦𝑖 is the weight of plant-𝑖 in Jamu 𝑌, 𝑥 is mean of Jamu 𝑋, and 𝑦 is mean of Jamu 𝑌. The higher similarity between Jamu pairs the higher the correlation value. In the present study, 𝑥𝑖 and 𝑦𝑖 are assigned as 1 or 0 in cases the 𝑖th plant is, respectively, included or not included in the formula. Under such condition, Pearson correlation corresponds to fourfold point correlation 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 included in both 𝑋 and 𝑌, in only 𝑋, in only 𝑌, and in neither 𝑋 nor 𝑌, respectively. In step 2 the Jamu clusters are generated using network clustering algorithm DPClusO. DPClusO can generate clusters characterized by high density and identified 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. The 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, defined by the following formula [18]: TPR =

TP , TP + FN

(4)

where true positive (TP) is the number of correctly classified and false negative (FN) is the number of incorrectly rejected entities. We refer to the proposed method as supervised clustering because after 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

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Input: Jamu formulas

Step 1 Constructing ingredient correlation network

Step 2 Extracting highly connected Jamu

A

B

C

Step 3 Supervised analysis for voting utilization

A

Step 4 Listing ingredients

D

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 classification of medicinal plants to dominant disease.

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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 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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 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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 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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: The 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 coefficient (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 Classification of Diseases (ICD) version 10 from World Health Organization (WHO, Table 1) [19]. Those 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 classification, most Jamu formulas are useful for relieving muscle and bone, nutritional and metabolic diseases, and the digestive system. Furthermore, there is no Jamu formula classified 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: first matrix is Jamu-plant relation with dimension 3138 × 465 and the second matrix is Jamu-disease relation with dimension 3138 × 18. After completion of data acquisition process, we calculated the similarity between Jamu pairs using correlation measure. The similarity measures between Jamu pairs were determined 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

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-𝑛 (0.7%, 0.5%, and 0.3%) pairs of Jamu formula to create 3 sets of Jamu pairs. 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 and the corresponding minimum correlation values are 0.596, 0.665, and 0.718, respectively. The 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 verified 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]. The difference of formulas between Jamu and Kampo can be explained by herb selection by medicinal researchers based on the optimization process of selection [4]. Thus, 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

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Frequency 13 23 40

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

200

100

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8

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5

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2 3

37 22 13 5 2 1

3

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2

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1

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221

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71

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5

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Frequency

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63 114

234

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81

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Frequency 12 22 43

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7

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10 (Deg.)

20

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50

100

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

to Erd˝os-R´enyi (ER) model [21], Barab´asi-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 software for analyzing the characteristics of both the Jamu and the random networks. We determined five statistical indexes, that is, average degree, clustering coefficient, number of connected component, network diameter, and network density of each Jamu network and also of each random network. The clustering coefficient 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 number of connected pairs between all neighbors of 𝑛. The 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 𝑛(𝑛 − 1)/2, where 𝑛 is the number of vertices, for an undirected graph). The 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 coefficient is roughly the same and in case of 0.3% the clustering coefficient is somewhat larger. The 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

Matching score

0.6

0.8

1.0

Matching score

(a) 0.7%

(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 different values corresponding to clustering coefficient, connected component, and network diameter imply that the Jamu networks are quite different from all 3 types of random networks. The differences between Jamu networks and ER random networks are the largest. Random networks constructed based on other two models are also substantially different from Jamu networks. Based on the fact that the random networks constructed based on all three types of models are different 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 coefficient 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 finding overlapping cohesive groups in an undirected simple graph

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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 officinale 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 officinale 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-specific 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 officinale 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 grandiflora 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-specific 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 officinale

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 tetragonolobus Parkia roxburghii Piper cubeba Kaempferia galanga Coriandrum sativum Cola acuminata Coffea arabica Orthosiphon stamineus Curcuma longa Piper nigrum Alpinia galanga Vitex trifolia Zingiber amaricans Zingiber zerumbet Zingiber aromaticum Languas galanga Massoia aromatica Morinda citrifolia Carum copticum Panax pseudoginseng Oryza sativa Myristica fragrans Pandanus amaryllifolius Eurycoma longifolia Hydrocotyle asiatica Areca catechu Mentha arvensis Lepiniopsis ternatensis Pimpinella pruatjan Andrographis paniculata Blumea balsamifera Cymbopogon nardus Sida rhombifolia Cinnamomum sintoc Piper betle Talinum paniculatum Elephantopus scaber Cyperus rotundus Curcuma aeruginosa Kaempferia pandurata

∗ ∗

∗ ∗ ∗

∗

∗ ∗

∗ ∗

∗

∗

BioMed Research International

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Table 4: Continued.

Table 4: Continued.

Number Plants name

Hit-miss status

Number Plants name

Hit-miss status

62 Curcuma xanthorrhiza 63 Tribulus terrestris 64 Corydalis yanhusuo 65 Pausinystalia yohimbe H. Disease: nutritional and metabolic diseases

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 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

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

44 45 46 47 48 49 50 51 52 53 54

Piper betle Spirulina Stevia rebaudiana Theae sinensis Sonchus arvensis Curcuma heyneana Curcuma aeruginosa Kaempferia pandurata Curcuma xanthorrhiza Curcuma zedoaria Olea europaea I. Disease respiratory diseases

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

Foeniculum vulgare Clausena anisum-olens Glycyrrhiza uralensis Zingiber purpureum Piper retrofractum Syzygium aromaticum Gaultheria punctata Panax ginseng Equisetum debile Zingiber officinale Citrus aurantium Nigella sativa Amomum compactum Cinnamomum burmannii Melaleuca leucadendra Parkia roxburghii Cocos nucifera Piper cubeba Kaempferia galanga Coriandrum sativum Curcuma longa Piper nigrum Zingiber aromaticum Languas galanga Mentha piperita Oryza sativa Myristica fragrans Pandanus amaryllifolius Hydrocotyle asiatica Mentha arvensis Lepiniopsis ternatensis Helicteres isora Blumea balsamifera Cymbopogon nardus Piper betle Curcuma xanthorrhiza

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

Foeniculum vulgare Glycyrrhiza uralensis Zingiber purpureum Allium sativum Tinospora tuberculata Pandanus conoideus Syzygium aromaticum Punica granatum Zingiber officinale Guazuma ulmifolia Nigella sativa Amomum compactum Cinnamomum burmannii Parameria laevigata Caesalpinia sappan Soya max Cocos nucifera Rheum tanguticum Piper cubeba Murraya paniculata Kaempferia galanga Coffea arabica Orthosiphon stamineus Curcuma longa Piper nigrum Zingiber aromaticum Aloe vera Phaleria papuana Galla lusitania Quercus lusitanica Morinda citrifolia Myristica fragrans Momordica charantia Areca catechu Lepiniopsis ternatensis Alstonia scholaris Hibiscus sabdariffa Laminaria japonica Syzygium polyanthum Andrographis paniculata Sindora sumatrana Cassia angustifolia Woodfordia floribunda

∗

∗

∗ ∗ ∗

∗

∗

∗

∗ ∗

∗

∗ ∗ ∗

∗ ∗ ∗

12

BioMed Research International Table 4: Continued.

70

Hit-miss status

37 38

Salix alba Matricaria chamomilla J. Disease: skin and connective tissue

Hit Miss

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

Strychnos ligustrina Merremia mammosa Piper retrofractum Santalum album Zingiber officinale Citrus aurantium Citrus hystrix Cassia siamea Cocos nucifera Trigonella foenum-graecum Orthosiphon stamineus Curcuma longa Vetiveria zizanioides Aloe vera Rosa chinensis Jasminum sambac Phyllanthus urinaria Mentha piperita Oryza sativa Myristica fragrans Hydrocotyle asiatica Lepiniopsis ternatensis Alstonia scholaris Andrographis paniculata Cymbopogon nardus Piper betle Theae sinensis Curcuma heyneana Kaempferia pandurata Curcuma xanthorrhiza Melaleuca leucadendra Matricaria chamomilla K. Disease: the urinary system

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 Miss

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

Foeniculum vulgare Imperata cylindrica Strychnos ligustrina Plantago major Zingiber officinale Cinnamomum burmannii Strobilanthes crispus Kaempferia galanga Orthosiphon stamineus Phyllanthus urinaria Blumea balsamifera Sonchus arvensis Curcuma xanthorrhiza

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

∗

∗ ∗

∗ ∗

50 40 30

24

20

14

18

10 0

∗

63

60 Number of plants

Number Plants name

1

2

3

4

5

6

5

6

2

2

1

7

8

9

Number of diseases

Figure 6: Distribution of 135 plants assigned based on 0.7% dataset with respect to the number of diseases they are assigned to.

∗

∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗

indicates that plant will not assigned if we use matching score >0.7.

for any type of application. It ensures coverage and performs robustly in case of random addition, removal, and rearrangement of edges in protein-protein interaction (PPI) networks [14]. While applying DPClusO, the parameter values of density and cluster property that we used in this experiment are 0.9 and 0.5, respectively [15]. Table 3 shows the summary of clustering result by DPClusO. Because clusters consisting of two Jamu formulas are trivial clusters, for the next steps we only use clusters each of which consists of 3 or more Jamu formulas. The number of total clusters increases along with the larger dataset, although the threshold correlation between Jamu pairs decreases. We evaluated the clustering result using matching score to determine dominant disease for every cluster (step 3 in Figure 1). Matching score of a cluster is the ratio of the highest number of Jamu associated with the same disease to the total number of Jamu in the cluster. Thus matching score is a measure to indicate how strongly a disease is associated to a cluster. Figure 4 shows the distribution of the clusters with respect to matching score from three datasets. All datasets have the highest frequency of clusters at matching score >0.9 and overall most of the clusters have higher matching score, which means most of the DPClusO generated clusters can be confidently related to a dominant disease. Furthermore the number of clusters with matching score >0.9 is remarkably larger compared to the same in other ranges of matching score in case of the 0.3% dataset (Figure 4(c)). If we compare the ratio of frequency of clusters at matching score >0.9 for every dataset, the 0.3% dataset has the highest ratio with 40.84% (of 453), compared to 29.67% (of 873) and 21.91% (of 1296), in case of 0.5% and 0.7% datasets, respectively. Thus, the most reliable species to disease relations can be predicted at matching score >0.9 corresponding to the clusters generated from 0.3% dataset. Figure 5(a) shows the success rate for all 3 datasets with respect to threshold matching scores. Success rate is defined as the ratio of the number of clusters with matching score larger than the threshold to the total number of clusters. As expected it tends to produce lower success rate if we decrease correlation value to create the datasets. However more clusters are generated and more information can be extracted when we lower the threshold correlation value. The success rate increases rapidly as the matching score decreases

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Table 5: Relation between disease classes in NCBI and efficacy classes reported by Afendi et al. [6]. Class of disease D1 Blood and lymph diseases

Ref. NCBI

Efficacy class E7 Pain/inflammation (PIN)

D2 Cancers

NCBI

D3 The digestive system

NCBI

E7 Pain/inflammation (PIN) E4 Gastrointestinal disorders (GST)

D4 Ear, nose, and throat D5 Diseases of the eye

NCBI NCBI

E7 Pain/inflammation (PIN) E7 Pain/inflammation (PIN) E7 Pain/inflammation (PIN)

D6 Female-specific diseases D7 Glands and hormones

NCBI NCBI

E5 Female reproductive organ problems (FML) E7 Pain/inflammation (PIN)

D8 The heart and blood vessels D9 Diseases of the immune system D10 Male-specific diseases

NCBI NCBI NCBI

E7 Pain/inflammation (PIN) E7 Pain/inflammation (PIN) E6 Musculoskeletal and connective tissue disorders (MSC)

D11 Muscle and bone D12 Neonatal diseases

NCBI NCBI

E6 Musculoskeletal and connective tissue disorders (MSC) E7 Pain/inflammation (PIN)

D13 The nervous system

NCBI

D14 Nutritional and metabolic diseases

NCBI

E7 Pain/inflammation (PIN) E2 Disorders of appetite (DOA) E4 Gastrointestinal disorders (GST)

D15 Respiratory diseases

NCBI

D16 Skin and connective tissue D17 The urinary system D18 Mental and behavioural disorders

NCBI ∗ ∗

from 0.9 to 0.6 and after that the slope of increase of success rate decreases. Therefore in this study we empirically decide 0.6 as the threshold matching score to predict plant-disease relations. 3.3. Assignment of Plants to Disease. By using DPClusO resulting clusters, we assigned plants to classes of disease. Based on a threshold matching score we assigned dominant disease to a cluster. Then we assign a plant to a cluster by way of analyzing the ingredients of the Jamu formulas belonging to that cluster and determining the highest frequency plant, that is, the plant that is used for maximum number Jamu belonging to that cluster (step 4 in Figure 1). Thus we assign a disease and a plant to each cluster having matching score greater than a threshold. Our hypothesis is that the disease and the plant assigned to the same cluster are related. The total number of assigned plants depends on matching score value. Figure 5(b) shows the number of predicted plants that can be assigned to diseases in the context of matching score. With higher matching score value, the number of predicted plants assigned to classes of disease is supposed to remain similar or decrease but the reliability of prediction increases. In Figure 5(b) a sudden change in the number of predicted plants is seen at matching score 0.6 which we consider as empirical threshold in this work. Based on the 0.7% dataset, the largest number of plants (135 plants, Table 4) was assigned to diseases. There are 63 plants assigned to only one class of disease, whereas the other 72 plants are assigned to at least two or more classes of disease (Figure 6).

E8 Respiratory disease (RSP) E7 Pain/inflammation (PIN) E9 Wounds and skin infections (WND) E1 Urinary related problems (URI) E3 Disorders of mood and behavior (DMB)

3.4. Evaluation of the Supervised Clustering Based on DPClusO. We used previously published results [6] as gold standard to evaluate our results. The previous study assigned plants to 9 kinds of efficacy whereas we assigned the plants to 18 disease classes (16 from NCBI and 2 additional classes). For the sake of evaluation we got done a mapping of the 18 disease classes to 9 efficacy classes by a professional doctor, which is shown in Table 5. Table 6 shows the prediction result of plant-disease relations for all 3 datasets, corresponding to clusters with matching score greater than 0.6. Table 6 also shows 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/efficacy class by calculating the ratio of the number of correct prediction to the number of all predictions. When a disease corresponds to more than one kind of efficacy, the highest TPR can be considered the TPR for the corresponding disease. For all 3 datasets the TPR corresponding to each disease is roughly 90% or more. The 0.3% dataset consists of Jamu pairs with higher correlation values and based on this dataset 117 plants are assigned to 14 disease classes. The 0.7% dataset contains more Jamu pairs and assigned plants to 11 disease classes, one less disease class compared to 0.5% dataset. The two disease classes covered by 0.3% dataset but not covered by 0.5% and 0.7% datasets are the nervous system (D13) and disease of the immune system (D9). The only disease class covered by 0.3% and 0.5% datasets but not covered by 0.7% dataset is mental and behavioural disorders (D18). The larger dataset network tends to have

14

BioMed Research International Table 6: The prediction result of plant-disease relations using matching score >0.6. 0.7% dataset

Class of disease

Corresponding efficacy

Number of Correct assigned prediction plants

0.5% dataset True positive rate

Number of Correct assigned prediction plants

0.3% dataset True positive rate

Number of Correct assigned prediction plants

True positive rate 0.83

D1

E7

26

22

0.85

24

20

0.83

24

20

D2

E7

1

1

1.00

5

5

1.00

1

1

1.00

28

1.00

25

0.89

0

—

D3

E4 E7

42 0

42

1.00

38

0.90

0

—

33 0

33

1.00

30

0.91

0

—

28

D4

E7

0

D5

E7

0

0

—

0

0

—

0

0

—

D6

E5

38

38

1.00

37

37

1.00

32

32

1.00

D7

E7

0

0

—

0

0

—

0

0

—

D8

E7

10

8

0.80

8

7

0.88

6

5

0.83

D9

E7

0

0

—

0

0

—

1

1

1.00

D10

E6

6

4

0.67

2

0

—

3

1

0.33

D11

E6

65

65

1.00

71

71

1.00

60

60

1.00

D12

E7

0

0

—

0

0

—

0

0

—

D13

E7

0

0

—

0

0

—

5

5

1.00

36

0.80

26

0.74

45

1.00

35

1.00

34

1.00

33

1.00

29

0.88

27

1.00

D14 D15

E2 E4 E7 E8

54 38

44

0.81

54

1.00

37

0.97

45 34

35 33

31

0.82

30

0.88

D16

E9

32

31

0.97

32

32

1.00

D17

E1

13

13

1.00

9

9

1.00

8

8

1.00

0

0 135

—

5

5 129

1.00

4

4 117

1.00

D18 E3 Total assigned plants

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

4. Conclusions This paper introduces a novel method called supervised clustering for analyzing big biological data by integrating network clustering and selection of clusters based on supervised learning. In the present work we applied the method for data mining of Jamu formulas accumulated in KNApSAcK database. Jamu networks were constructed based on correlation similarities between Jamu formulas and then network clustering algorithm DPClusO was applied to generate high density Jamu modules. For the analysis of the next steps potential clusters were selected by supervised learning. The successful clusters containing several Jamu related to the same disease might be useful for finding main ingredient plant for that disease and the lower matching score value clusters will be associated with varying plants

27

which might be supporting ingredients. By applying the proposed method important plants from Jamu formulas for every classes of disease were determined. The plant to disease relations predicted by proposed network based method were evaluated in the context of previously published results and were found to produce a TPR of 90%. For the larger dataset networks, success rate and the coverage of disease classes become lower but prediction of more plant-disease relations can be achieved.

Conflict of Interests The authors declare that there is no financial interest or conflict of interests regarding the publication of this paper.

Acknowledgments This work was supported by the National Bioscience Database Center in Japan and the Ministry of Education, Culture, Sports, Science, and Technology of Japan (Grant-in-Aid for Scientific Research on Innovation Areas “Biosynthetic Machinery. Deciphering and Regulating the System for Creating Structural Diversity of Bioactivity Metabolites (2007)”).

BioMed Research International

References [1] R. Verporte, H. K. Kim, and Y. H. Choi, “Plants as source of medicines,” in Medicinal 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 complementary therapies?” in Complementary Medicine: An Objective Appraisal, E. Ernst, Ed., pp. 71–88, Butterworth-Heinemann, Oxford, UK, 1996. [3] E. Ernst, “Herbal medicines put into context,” British Medical Journal, vol. 327, no. 7420, pp. 881–882, 2003. [4] F. M. Afendi, T. Okada, M. Yamazaki et al., “KNApSAcK family databases: integrated metabolite—plant species databases for multifaceted plant research,” Plant and Cell Physiology, vol. 53, no. 2, p. e1, 2012. [5] F. M. Afendi, N. Ono, Y. Nakamura et al., “Data mining methods for omics and knowledge of crude medicinal plants toward big data biology,” Computational and Structural Biotechnology Journal, vol. 4, no. 5, Article ID e201301010, 2013. [6] F. M. Afendi, L. K. Darusman, A. Hirai et al., “System biology approach for elucidating the relationship between Indonesian herbal plants and the efficacy of Jamu,” in Proceedings of the 10th 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 of Jamu formulations by PLS modeling,” Current Computer-Aided Drug Design, vol. 9, pp. 46–59, 2013. [8] F. M. Afendi, L. K. Darusman, M. Fukuyama, M. Altaf-UlAmin, and S. Kanaya, “A bootstrapping approach for investigating the consistency of assignment of plants to Jamu efficacy by PLS-DA model,” Malaysian Journal of Mathematical 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,” BMC Systems Biology, vol. 7, article 90, 2013. [10] C. Bachmaier, U. Brandes, and F. Schreiber, “Biological network,” 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 for constructing gene regulatory networks from microarray data by using Bayesian network,” Bioinformatics, vol. 22, no. 23, pp. 2952–2954, 2006. [12] P. Langfelder and S. Horvath, “WGCNA: an R package for weighted 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 tool for gene network building, visualization and analysis,” BMC Bioinformatics, vol. 11, article 91, 2010. [14] M. Altaf-Ul-Amin, M. Wada, and S. Kanaya, “Partitioning a PPI network into overlapping modules constrained by highdensity 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 graph clustering software mainly focused on detection of protein complexes in interaction networks,” Journal of Computer Aided Chemistry, vol. 7, pp. 150–156, 2006. [16] S. K. Kachigan, Multivariate Statistical Analysis: A Conceptual Introduction, Radius Press, New York, NY, USA, 1991.

15 [17] J. L. Rodgers and W. A. Nicewander, “Thirteen ways to look at the correlations coefficient,” The American Statiscian, vol. 42, pp. 59–66, 1995. [18] M. Li, J.-E. Chen, J.-X. Wang, B. Hu, and G. Chen, “Modifying the DPClus algorithm for identifying protein complexes based on new topological structures,” BMC Bioinformatics, vol. 9, article 398, 2008. [19] World Health Organization, “International Classification of Diseases (ICD) 10,” 2010, http://www.who.int/classifications/ icd/en/. [20] National Center for Biotechnology Information, Genes and Disease, 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. Barab´asi and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999. [23] A. V´azquez, “Growing network with local rules: preferential attachment, clustering hierarchy, and degree correlations,” Physical 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.