A multiple linear regression model for structure of n-linked oligosaccharides
A multiple linear regression model for structure of n-linked oligosaccharides
- Author(s): Xiaoqing Cheng ; Wai-Ki Ching ; Wenpin Hou ; K.F. Aoki-Kinoshita
- DOI: 10.1049/cp.2015.0618
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- Author(s): Xiaoqing Cheng ; Wai-Ki Ching ; Wenpin Hou ; K.F. Aoki-Kinoshita Source: 12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management (ISORA 2015), 2015 page ()
- Conference: 12th International Symposium on Operations Research and its Applications in Engineering, Technology and Management (ISORA 2015)
- DOI: 10.1049/cp.2015.0618
- ISBN: 978-1-78561-085-1
- Location: Luoyang, China
- Conference date: 21-24 Aug. 2015
- Format: PDF
It is well-known that carbohydrate sugar chains, or glycans, play various well in cellular processes, including cancer, but the elucidation of glycans is difficult because of their complex structure. Both computational methods and mathematical models are necessary to integrate and analyze the information of glycomics data so as to efficiently detect glycan structures. In this paper, we propose a new model to predict the structure of N-glycans, which are the most common type of glycans. Our proposed prediction method is based on a Multiple Linear Regression (MLR) model. The coefficients of our proposed model are solved by using experimental data. We obtain our data from High Performance Liquid Chromatography (HPLC) experiments. Three sources of our data are adopted and they are divided into two parts: elution value on an Amide column and elution value on an OctaDecylSilane (ODS) column. After pre-processing the data, we then construct our proposed MLR model. The obtained correlation coefficients are 0.9680 for the Amide data and 0.9263 for the ODS data. We have also tested the correctness of the model statistically. The model test and correlation coefficients demonstrate both the accuracy and efficiency of our proposed model.
Inspec keywords: proteins; regression analysis; molecular configurations; molecular biophysics; chromatography
Subjects: Chromatography; General, theoretical, and mathematical biophysics; Macromolecular conformation (statistics and dynamics); Biomolecular structure, configuration, conformation, and active sites; Other statistical model calculations (atoms and molecules)
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