| Title | Evaluation of metabolites extraction strategies for identifying different brain regions and their relationship with alcohol preference and gender difference using NMR metabolomics |
| Authors | Wang, Jie Zeng, Hao-Long Du, Hongying Liu, Zeyuan Cheng, Ji Liu, Taotao Hu, Ting Kamal, Ghulam Mustafa Li, Xihai Liu, Huili Xu, Fuqiang |
| Affiliation | Chinese Acad Sci, State Key Lab Magnet Resonance & Atom & Mol Phys, Key Lab Magnet Resonance Biol Syst, Wuhan Ctr Magnet Resonance,Wuhan Inst Phys & Math, Wuhan, Hubei, Peoples R China. Huazhong Univ Sci & Technol, Tongji Med Coll, Tongii Hosp, Dept Lab Med, Wuhan, Hubei, Peoples R China. Huazhong Agr Univ, Coll Food Sci & Technol, Wuhan, Hubei, Peoples R China. Peking Univ, Hosp 3, Dept Anesthesiol, Beijing, Peoples R China. Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan, Hubei, Peoples R China. Univ Faisalabad, Govt Coll, Dept Chem, Faisalabad 38000, Pakistan. Fujian Univ Tradit Chinese Med, Acad Integrat Med, Fuzhou, Fujian, Peoples R China. Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China. |
| Keywords | Metabolomics Decision tree Principal component analysis (PCA) Alcohol preference Brain regions MAGNETIC-RESONANCE-SPECTROSCOPY CHRONIC ETHANOL EXPOSURE RAT-BRAIN SEX-DIFFERENCES DECISION TREES SPECTRA DISCRIMINATION DISORDERS GLUTAMATE TOXICITY |
| Issue Date | 2018 |
| Publisher | TALANTA |
| Citation | TALANTA. 2018, 179, 369-376. |
| Abstract | Metabolomics generate a profile of small molecules from cellular/tissue metabolism, which could directly reflect the mechanisms of complex networks of biochemical reactions. Traditional metabolomics methods, such as OPTS-DA, PIS-DA are mainly used for binary class discrimination. Multiple groups are always involved in the biological system, especially for brain research. Multiple brain regions are involved in the neuronal study of brain metabolic dysfunctions such as alcoholism, Alzheimer's disease, etc. In the current study, 10 different brain regions were utilized for comparative studies between alcohol preferring and non-preferring rats, male and female rats respectively. As many classes are involved (ten different regions and four types of animals), traditional metabolomics methods are no longer efficient for showing differentiation. Here, a novel strategy based on the decision tree algorithm was employed for successfully constructing different classification models to screen out the major characteristics of ten brain regions at the same time. Subsequently, this method was also utilized to select the major effective brain regions related to alcohol preference and gender difference. Compared with the traditional multivariate statistical methods, the decision tree could construct acceptable and understandable classification models for multi-class data analysis. Therefore, the current technology could also be applied to other general metabolomics studies involving multi class data. |
| URI | http://hdl.handle.net/20.500.11897/502023 |
| ISSN | 0039-9140 |
| DOI | 10.1016/j.talanta.2017.11.045 |
| Indexed | SCI(E) EI PubMed |
| Appears in Collections: | 第三医院 |
