Data-intensive sciences have progressed in modern astronomy [1], biology [2][3][4][5][6][7][8], computational materials science [9], ecology [10][11] and social science [12] because open-access data has increased drastically. Data-intensive or -driven discovery in biology requires a large open pool of data across the full breadth of the life sciences and the access to the pool will invite "New" logic, strategies and tools to discover new trends, associations, discontinuities, and exceptions that reveal aspects of the underlying biology [2, 5, 6]. Big data biology, which is a discipline of data-intensive science, was proposed based on the rapid increasing of omics data produced by genomics, transcriptomics, proteomics and metabolomics [2][3][4][5][6][7][8]. This situation is also a feature of the ethnomedicinal survey and the number of medicinal plants is estimated to be 40,000 to 70,000 around the world [13] and many countries utilize these plants as blended herbal medicines, e.g., China (traditional Chinese medicine), Japan (Kampo medicine), India (Ayruveda, Siddha and Unani) and Indonesia (Jamu). 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. To comprehensively understand the medicinal usage of plants based upon traditional and modern knowledge, we add to KNApSAcK Family database systems the selected herbal ingredients i.e., the formulas of Kampo and Jamu, omics information in plants and humans, and physiological activities in humans [14][15][16]. These information need to be connected in a way that enables scientists to make predictions based on general principles. In this mini-review, we discuss the usage of KNApSAcK Family DB in metabolomics, explain mining techniques such as principal component analysis (PCA), partial least square regression (PLSR) and multiway model, and show their application on Indonesian blended herbal medicines (Jamu) as a case study. Omics biology, like most scientific disciplines, is in an era of accelerated increase of data, so called big data biology [2-8]. Largescale sequencing centers, high-throughput analytical facilities and CSBJ 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.
For the traditional medicine, data mining method is also utilized widely; Afendi et al. discussed the usage of KNApSAcK Family DB in metabolomics, explain mining techniques such as principal component analysis (PCA), partial least square regression (PLSR), and multiway model, and show their application on Indonesian blended herbal medicines (Jamu) as a case study REF .