Biopharmaca BiofarmakaData Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology


Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

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Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology



Farit M. Afendia,b,c, Naoaki Onoa, Yukiko Nakamuraa, Kensuke Nakamurad, Latifah K. Darusmanc, Nelson Kibingea, Aki Hirai Moritaa, Ken Tanakae, Hisayuki Horaif, Md. Altaf-Ul-Amina, Shigehiko Kanayaa,*



aGraduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0101, Ikoma, Japan
bDepartment of Statistics, Bogor Agricultural University, Jln. Meranti, Kampus IPB Darmaga, Bogor 16680, Indonesia
cBiopharmaca Research Center, Bogor Agricultural University, Kampus IPB Taman Kencana, Jln. Taman Kencana No. 3 Bogor 16151, Indonesia
dMaebashi Institute of technology, 450-1 Kamisadori, Maebashi-shi, Gunma, 371-0816 Japan
eDepartment of Medicinal Resources, Institute of Natural Medicine, University of Toyama, 2630 Toyama, 930-0194, Japan
fDepartment of Electronic and Computer Engineering, Ibaraki National College of Technology, 866 Nakane, Hitachinaka, Ibaraki 312-8508, Japan
* Corresponding author: E-mail address: This email address is being protected from spambots. You need JavaScript enabled to view it. (Shigehiko Kanaya)

Issue Date : January, 2013
Publisher : Computational and Structural Biotechnology Journal
Series / Report No :

DOI: 10.5936/csbj.201301010
Physical Description :
2012, Volume: 4, Issue: 5
Language : en


Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.
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Farit M. Afendi, Naoaki Ono, Yukiko Nakamura, Kensuke Nakamura, Latifah K. Darusman, Nelson Kibinge, Aki H. Morita, Ken Tanaka, Hisayuki Horai, Md Altaf-Ul-Amin, Shigehiko Kanaya. Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology. Computational and Structural Biotechnology Journal, Vol. 4, No. 5. (01 January 2013), doi:10.5936/csbj.201301010
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