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Indian medicinal plants for diabetes: text data mining the ...

Indian medicinal plants for diabetes: text data mining the literature ofdifferent electronic databases for future Selvaraj1*, Sakthivel Periyasamy21 Department of Computer Science and Engineering, Faculty of Computing, Sathyabama University, Jeppiaar Nagar,Rajiv Gandhi Salai, Chennai, India2 Department of Electronics and Communication Engineering, Anna University, Guindy, Chennai, IndiaAbstractDiabetes, a metabolic disorder, affects nearly 7% of world population and predicted that it would be theseventh leading cause of death by the year of 2030. The prevalence and morbidity of diabetes areincreasing rapidly because of the lifestyle and diet changes occurring with urbanization. Medicinalplants and their derivatives have been proven to be an effective and safe therapy offering variousbenefits, for example, the moderate reduction in hypoglycaemia, in the treatment and prevention ofdiabetes.

Indian medicinal plants for diabetes: text data mining the literature of different electronic databases for future therapeutics. Bhanumathi Selvaraj 1*, Sakthivel Periyasamy2 1Department of Computer Science and Engineering, Faculty of Computing, Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, India

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Transcription of Indian medicinal plants for diabetes: text data mining the ...

1 Indian medicinal plants for diabetes: text data mining the literature ofdifferent electronic databases for future Selvaraj1*, Sakthivel Periyasamy21 Department of Computer Science and Engineering, Faculty of Computing, Sathyabama University, Jeppiaar Nagar,Rajiv Gandhi Salai, Chennai, India2 Department of Electronics and Communication Engineering, Anna University, Guindy, Chennai, IndiaAbstractDiabetes, a metabolic disorder, affects nearly 7% of world population and predicted that it would be theseventh leading cause of death by the year of 2030. The prevalence and morbidity of diabetes areincreasing rapidly because of the lifestyle and diet changes occurring with urbanization. Medicinalplants and their derivatives have been proven to be an effective and safe therapy offering variousbenefits, for example, the moderate reduction in hypoglycaemia, in the treatment and prevention ofdiabetes.

2 However, the identification of such valuable Indian medicinal plants for diabetes frombiomedical literature is not comprehensively explored. In this study, we have investigated Indianmedicinal plants for diabetes in the biomedical literature using text data mining technique. Wediscovered a total of 203 Indian medicinal plants for diabetes in 355 articles out of 15651 articles of textcorpus in the dataset. In addition, we analysed the importance of Indian medicinal plants for thetreatment of diabetes by means of the frequency of 203 plants in 355 articles, which identified 22 anti-diabetic Indian medicinal plants that showed 9 frequencies. Momordica charantia, also known asbitter melon, had the highest frequency ( 51 frequencies) among 203 Indian plants , indicating that it isthe most important Indian medicinal plant for the treatment of diabetes.

3 In addition, we compared theidentified 203 plants with previously reported database of anti-diabetic Indian medicinal plants , whichshowed the identification of 100 new anti-diabetic Indian medicinal plants . The results from this studycould provide helpful information for future experimental and clinical studies, and the development offuture therapeutic for : Indian , medicinal plants , Diabetes, Text data mining , database , Biomedical, on December 10, 2016 IntroductionDiabetes, also known as diabetes mellitus, is a progressivemetabolic disorder, which is characterized by elevated levels ofglucose or sugar in the blood. The disease is caused byinadequate secretion of insulin, body s poor response toinsulin, or both, which severely damage the body s systems,including the heart, blood vessels, eyes, kidneys, and 2 diabetes is the most common in adults that occurs whenthe body becomes resistant to insulin or does not secreteadequate insulin while Type 1 diabetes is a juvenile or insulin-dependent that occurs when the pancreas produces little or noinsulin by itself.

4 Diabetes is rapidly growing worldwide, andaffected 422 million people in 2014 and resulted in over 3million deaths [1]. The World Health Organization (WHO)estimated that diabetes would be the seventh leading cause ofdeath by the year of 2030, and suggested that healthy lifestyleand right medication and regular screening can prevent andavoid the consequence of diabetes, respectively [1].For many decades, medicinal plants have been beneficialresources for the treatment of several diseases, includingdiabetes [2-5]. Some well-known drugs in current-use fordiabetes have been developed from plants such as metformindrug derived from the Galega officinalis [6]. Many studieshave also indicated the advantages of medicinal plants in thetherapeutic development, for example availability andacceptable risk-benefit ratio.

5 Though the ethnobotanicalcommunity has reported a list of anti-diabetic medicinal plants [7], in the search for new treatments and cures, yet moremedicinal plants are being explored for their therapeuticdevelopment. At this juncture, efforts have been also made tocompile information on the medicinal plants to exclusivelydevelop databases for diabetes. For example, InDiaMed [8] andDIAB [9] databases which have been developed using manualcuration from various sources PubMed [10], Scopus [11],Science Direct [12] and Wiley [13], and also from folkloremedicinal usage. However, limited efforts have gone tocomprehensively gather Indian medicinal plants for Research 2016; Special Issue: S430-S436 Biomed Res- India 2016 Special IssueSpecial Section:Health Science and Bio Convergence TechnologyText data mining is an approach, which is often used to extractand analyse and/or evaluate information in the area ofbiomedical discovery [14].

6 It is capable of producingsignificant results that would be helpful to answer particularresearch queries, for example, finding medicinal plants inbiomedical literature for experimental and clinical research[15]. The text data mining methodology has been successfullyemployed to discover and analyse novel herbs or plants orformulas, from the traditional Chinese medicine or historicalliterature or electronic databases, for different diseases such asvascular dementia [16], dysmenorrhea [17], chronic cough[18], respiratory disease [19], diabetic nephropathy [20], age-related dementia and memory impairment [21]. However, thereis no such a study on Indian medicinal plants for diabetes inelectronic literature this study, we have analysed the Indian medicinal plants fordiabetes from different electronic literature databases using textdata mining approach.

7 Our text data mining strategysuccessfully extracted 355 unique articles, which contain onlyIndian medicinal plants for diabetes, from the 15651 articles ofthe text corpus, which resulted in the identification of 203 Indian medicinal plants for diabetes. In addition, thefrequencies of plants were analysed from the 355 articles, andit was found that 22 Indian medicinal plants were 9frequencies, emphasizing their importance for the treatment ofdiabetes. In addition to this, the identified 203 plants werecompared to the exclusive database previously developed forthe Indian medicinal plants for diabetes. The results showedthat the identification of several new Indian medicinal plantsfor diabetes.

8 The overall results of the study could providefruitful information for future experimental and clinicalstudies, and the development of future therapeutic for and MethodsThe biomedical research has produced the abundance ofknowledge in the published literature , which stress the need ofnovel methods for knowledge extraction, visualization, andanalysis to uncover new and meaningful hypotheses. A textmining technique, a subfield of data mining , that seeks toextract useful new information from unstructured or semi-structured sources to address the most crucial questions. Thismining approach is designed and implemented extensively toextract and analyse comprehensive information in thebiomedical literature of diseases, natural products, herbs, this study, the text data mining approach was adopted foranalysing Indian medicinal plants for diabetes in thebiomedical literatures, and our mining strategy comprises thefollowing steps: data collection, data synthesis, data extractionand data pre-processing, and data analysis, as schematicallyshown in Figure 1.

9 These steps have been explained collectionLiteratures were searched with different electronic databasesincluding PubMed/MEDLINE [10], Scopus [11], ScienceDirect (SciDir) [12] and Wiley [13] using the followingdifferent search query terms: Indian medicinal plants fordiabetes, Indian traditional medicinal plants for diabetes,treatment of diabetes with Indian medicinal plants , treatment ofdiabetes with Indian medicinal herbs, Indian herbal plants fordiabetes, Indian herbs for anti-diabetic, etc. These query termswere derived from the different biomedical literature andmanuals. Each search query term was typed in the search boxof the electronic databases to separately retrieve informationrelevant to the Indian medicinal plants for diabetes.

10 Onlyjournal articles were considered and retrieved. different searchquery terms to the electronic databases produced search resultsthat were subsequently combined to form a single text corpusfor each synthesisData synthesis is a process of collecting essential data from thedifferent sources and this is an important step in the textmining. In the above collected text corpus, each articlecontains much information such as article title, abstract, journalname, author's name, URL or PubMed ID, DOI, etc. Articles,which only contain both the title and abstract, were consideredand all other information were removed as our aim was tospecifically focus on the extraction of Indian medicinal several search query terms were used to retrieve the sameinformation from the databases, there is a possibility ofduplicate entries in the text corpus.


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