title: Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification.
abstract: BACKGROUND
  Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty.
  
  
  RESULTS
  Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods.
  
  
  CONCLUSIONS
  We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.
authors.affiliation: Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Shanghai Center for Bioinformation Technology, Shanghai 201203, China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Rudong
authors.identifier: 0000-0002-8996-920X
authors.initials: R
authors.lastname: Li
authors.affiliation: Department of Medical Microbiology and Parasitology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China.
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China.
authors.forename: Zongxin
authors.initials: Z
authors.lastname: Ling
authors.affiliation: Department of Medical Microbiology and Parasitology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China.
authors.forename: Xiaokui
authors.initials: X
authors.lastname: Guo
authors.affiliation: Shanghai Center for Bioinformation Technology, Shanghai 201203, China.
authors.forename: Lu
authors.initials: L
authors.lastname: Xie
authors.affiliation: Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China; Shanghai Center for Bioinformation Technology, Shanghai 201203, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001424:Bacterial Infections / Q000145:classification / Q000382:microbiology*; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D003731:Dental Caries; D006801:Humans; D054892:Metagenome / Q000235:genetics*; D056186:Metagenomics / Q000379:methods*; D010802:Phylogeny; D011014:Pneumonia; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D000069553:Supervised Machine Learning
chemical_list: D012336:RNA, Ribosomal, 16S