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

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 presen...

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Autores principales: Afendi, Farit M., Ono, Naoaki, Nakamura, Yukiko, Nakamura, Kensuke, Darusman, Latifah K., Kibinge, Nelson, Morita, Aki Hirai, Tanaka, Ken, Horai, Hisayuki, Altaf-Ul-Amin, Md., Kanaya, Shigehiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology (RNCSB) Organization 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962233/
https://www.ncbi.nlm.nih.gov/pubmed/24688691
http://dx.doi.org/10.5936/csbj.201301010
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author Afendi, Farit M.
Ono, Naoaki
Nakamura, Yukiko
Nakamura, Kensuke
Darusman, Latifah K.
Kibinge, Nelson
Morita, Aki Hirai
Tanaka, Ken
Horai, Hisayuki
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
author_facet Afendi, Farit M.
Ono, Naoaki
Nakamura, Yukiko
Nakamura, Kensuke
Darusman, Latifah K.
Kibinge, Nelson
Morita, Aki Hirai
Tanaka, Ken
Horai, Hisayuki
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
author_sort Afendi, Farit M.
collection PubMed
description 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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spelling pubmed-39622332014-03-31 Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology Afendi, Farit M. Ono, Naoaki Nakamura, Yukiko Nakamura, Kensuke Darusman, Latifah K. Kibinge, Nelson Morita, Aki Hirai Tanaka, Ken Horai, Hisayuki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Comput Struct Biotechnol J Mini Reviews 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. Research Network of Computational and Structural Biotechnology (RNCSB) Organization 2013-03-23 /pmc/articles/PMC3962233/ /pubmed/24688691 http://dx.doi.org/10.5936/csbj.201301010 Text en © Afendi et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.
spellingShingle Mini Reviews
Afendi, Farit M.
Ono, Naoaki
Nakamura, Yukiko
Nakamura, Kensuke
Darusman, Latifah K.
Kibinge, Nelson
Morita, Aki Hirai
Tanaka, Ken
Horai, Hisayuki
Altaf-Ul-Amin, Md.
Kanaya, Shigehiko
Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title_full Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title_fullStr Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title_full_unstemmed Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title_short Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology
title_sort data mining methods for omics and knowledge of crude medicinal plants toward big data biology
topic Mini Reviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962233/
https://www.ncbi.nlm.nih.gov/pubmed/24688691
http://dx.doi.org/10.5936/csbj.201301010
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