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

Abstract

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.

A general perspective of Big Data: applications, tools, challenges and trends

“…This situation is also a feature of the ethnomedicinal survey as the number of medicinal plants is estimated to be 40,000-70,000 around the world [121] and many countries utilize these plants as blended herbal medicines, e.g., China (traditional Chinese medicine), Japan (Kampo medicine), India (Ayurveda, Siddha and Unani) and Indonesia (Jamu). In [87], authors reviewed the usage of KNApSAcK Family DB in metabolomics and related area, discussed several statistical methods for handling multivariate data and showed their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot (a multivariate exploration tool) revealed that many plants are rarely utilized while some plants are highly utilized toward specific efficacy.…”

“…Literature on Big Data had a growth of more than 150 % from 2012 to 2014. Research papers addressed the challenges of capturing [75], storing [76,77], searching [78], sharing [23], analyzing [9,79] and visualizing [80,81] Big Data sets in several fields such as Computer Science [82,83]; Mathematics [63]; Business, Management and Accounting [41]; Engineering [84]; Physics and Astronomy [85,86]; Biochemistry, Molecular Biology and Genetics [87]; Medicine [88]; Social Sciences [89,90]; Materials Science [24]; Decision Sciences [91], and Arts and Humanities [92]. It is important to consider the distribution of papers by publisher to determine the paper focus: the challenge addressed by the works and the domain considered.…”

Prediction of Medicinal Properties Using Mathematical Models and Computation, and Selection of Plant Materials

“…Data mining is the process of sorting through large datasets to identify patterns and establish relationships to solve problems through data analysis by using machine-learning and statistical methods (Afendi et al, 2013;Yea et al, 2016). Data mining methods use various kinds of information obtained from sources such as bibliographic literature, experimental data, clinical data, and curated data.…”

Pencarian Aturan Asosiasi Semantic Web Untuk Obat Tradisional Indonesia

“…Analisis terhadap basisdata mengenai jamu untuk mendapatkan korelasi antara tanaman, jamu, dan korelasi antara tanaman, jamu, dan khasiat tentang jamu dilakukan dengan menggunakan model statistik [13] [14]. Metode yang digunakan adalah Biplot, Partial Least Square (PLS) dan metode bootstrapping untuk membuat kesimpulan dan fokus pada prediksi untuk membuat formula jamu.…”