## 1 Introduction  
The notions of system biology and personalized medicine are expected to change our views of health and diseases fundamentally in the near future. The term \"system biology\" has been coined to integrate data generated by all the \"omics\" platforms, taking advantage of the fast pace of the IT industry. Currently there are challenges highlighting in each realm of \"omics\". In metabolomics studies, samples were highly complex, biologically variable, and with large dynamic range, challenging separation, detection, and data analysis. Multiple techniques have been be used in metabolomics, such as nuclear magnetic resonance spectroscopy (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), each with its own advantages and drawbacks. For all of the metabolites with different polarity and in a wide molecular weight range, the combination of these techniques is needed. NMR is a non-destructive analytical technique, and sample preparation for NMR is straightforward and largely automated, but it is difficult to analyze NMR spectra of complex mixtures and NMR has a relative low sensitivity in the micro-molar range. Comparing to detectors such as NMR, MS provides better sensitivity and selectivity, and wider range of covered metabolites, at the benefit of the invention of electro-spray ionization (ESI) and atmosphere pressure chemical ionization (APCI). Direct-injection MS analyzes large number of metabolites in a very short time. However, direct infusion is hampered by ion suppression and matrix effects, where the signal of many analytes with low ionization efficiencies cannot be detected. To avoid these problems, MS is often hyphenated to GC or LC to decrease sample complexity. GC-MS is a sensitive and robust separation technique with established applications in the field of Metabolomics [4]. However, GC technique is not suitable for analysis of large or thermo labile compounds such as nucleotides or oligosaccharides. LC-MS usually does not require derivatization, has many modes of separation, and comes with a large sample loading capacity. Reversed-phase chromatography (RP) is a mature technique for separation of non-polar compounds. Monolithic capillary columns [5-11] and ultra performance liquid chromatography (UPLC) [12] have introduced high chromatographic peak resolution to LC, which in the past could be reached only by capillary GC columns. However, RP is not easily applicable to separate highly polar compounds. Ion-exchange chromatography is regularly used to separate organic anions and carbohydrates, but coupling to mass spectrometry (MS) is difficult due to high concentrations of non-volatile inorganic salts in the mobile phases. Hydrophilic interaction chromatography (HILIC) is recommended to separate simple and complex carbohydrates, amino acids, glycosides, and other polar natural products [13-20]. Coupling the separation techniques briefly described above to mass spectrometry (MS) provides extremely versatile and valuable tools for metabolomics studies [10; 11; 15; 21; 22]. Datasets generated with these techniques require modern computational tools and robust data mining technologies [23; 24]. Unsupervised methods, such as principle component analysis (PCA),are well-established techniques for dimensionality reduction and visualization, where the extracted information is represented by a set of new variables, termed as components [24]. In order to select prominent potential biomarkers among all the peaks, supervised methodologies with built-in preprocessing and feature selection are needed [26-28]. Feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection, is a technique commonly used in machine learning to select a subset of relevant feature for building robust learning models [29]. There are two major categories of feature selection, univariate and multivariate methods.