title: An improved dimensionality reduction method for meta-transcriptome indexing based diseases classification.
abstract: BACKGROUND
  Bacterial 16S Ribosomal RNAs profiling have been widely used in the classification of microbiota associated diseases. Dimensionality reduction is among the keys in mining high-dimensional 16S rRNAs' expression data. High levels of sparsity and redundancy are common in 16S rRNA gene microbial surveys. Traditional feature selection methods are generally restricted to measuring correlated abundances, and are limited in discrimination when so few microbes are actually shared across communities.
  
  
  RESULTS
  Here we present a Feature Merging and Selection algorithm (FMS) to deal with 16S rRNAs' expression data. By integrating Linear Discriminant Analysis method, FMS can reduce the feature dimension with higher accuracy and preserve the relationship between different features as well. Two 16S rRNAs' expression datasets of pneumonia and dental decay patients were used to test the validity of the algorithm. Combined with SVM, FMS discriminated different classes of both pneumonia and dental caries better than other popular feature selection methods.
  
  
  CONCLUSIONS
  FMS projects data into lower dimension with preservation of enough features, and thus improve the intelligibility of the result. The results showed that FMS is a more valid and reliable methods in feature reduction.
authors.affiliation: College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wang
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
authors.forename: Zongxin
authors.initials: Z
authors.lastname: Ling
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhu
authors.forename: Xiaokui
authors.initials: X
authors.lastname: Guo
authors.forename: Hong
authors.initials: H
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003731:Dental Caries / Q000382:microbiology; D020869:Gene Expression Profiling / Q000379:methods; D005819:Genetic Markers; D006801:Humans; D008954:Models, Biological; D011014:Pneumonia / Q000382:microbiology; D012329:RNA, Bacterial / Q000235:genetics*; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric; D059467:Transcriptome
chemical_list: D005819:Genetic Markers; D012329:RNA, Bacterial; D012336:RNA, Ribosomal, 16S