title: Mining gene expression data of multiple sclerosis.
abstract: OBJECTIVE
  Microarray produces a large amount of gene expression data, containing various biological implications. The challenge is to detect a panel of discriminative genes associated with disease. This study proposed a robust classification model for gene selection using gene expression data, and performed an analysis to identify disease-related genes using multiple sclerosis as an example.
  
  
  METHODS
  Gene expression profiles based on the transcriptome of peripheral blood mononuclear cells from a total of 44 samples from 26 multiple sclerosis patients and 18 individuals with other neurological diseases (control) were analyzed. Feature selection algorithms including Support Vector Machine based on Recursive Feature Elimination, Receiver Operating Characteristic Curve, and Boruta algorithms were jointly performed to select candidate genes associating with multiple sclerosis. Multiple classification models categorized samples into two different groups based on the identified genes. Models' performance was evaluated using cross-validation methods, and an optimal classifier for gene selection was determined.
  
  
  RESULTS
  An overlapping feature set was identified consisting of 8 genes that were differentially expressed between the two phenotype groups. The genes were significantly associated with the pathways of apoptosis and cytokine-cytokine receptor interaction. TNFSF10 was significantly associated with multiple sclerosis. A Support Vector Machine model was established based on the featured genes and gave a practical accuracy of ∼86%. This binary classification model also outperformed the other models in terms of Sensitivity, Specificity and F1 score.
  
  
  CONCLUSIONS
  The combined analytical framework integrating feature ranking algorithms and Support Vector Machine model could be used for selecting genes for other diseases.
authors.affiliation: Department of Public Health, Shantou University Medical College, Shantou City, Guangdong Province, China.
authors.forename: Pi
authors.initials: P
authors.lastname: Guo
authors.affiliation: Good Clinical Practice Office, Cancer Hospital of Shantou University Medical College, Shantou City, Guangdong Province, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Public Health, Shantou University Medical College, Shantou City, Guangdong Province, China.
authors.forename: Zhenli
authors.initials: Z
authors.lastname: Zhu
authors.affiliation: Laboratory of Cell Senescence, Shantou University Medical College, Shantou City, Guangdong Province, China.
authors.forename: Zhengliang
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Department of Public Health, Shantou University Medical College, Shantou City, Guangdong Province, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000378:metabolism*; D016022:Case-Control Studies; D057225:Data Mining / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000166:cytology / Q000378:metabolism*; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers