title: A mutation profile for top-k patient search exploiting Gene-Ontology and orthogonal non-negative matrix factorization.
abstract: BACKGROUND
  As the quantity of genomic mutation data increases, the likelihood of finding patients with similar genomic profiles, for various disease inferences, increases. However, so does the difficulty in identifying them. Similarity search based on patient mutation profiles can solve various translational bioinformatics tasks, including prognostics and treatment efficacy predictions for better clinical decision making through large volume of data. However, this is a challenging problem due to heterogeneous and sparse characteristics of the mutation data as well as their high dimensionality.
  
  
  RESULTS
  To solve this problem we introduce a compact representation and search strategy based on Gene-Ontology and orthogonal non-negative matrix factorization. Statistical significance between the identified cancer subtypes and their clinical features are computed for validation; results show that our method can identify and characterize clinically meaningful tumor subtypes comparable or better in most datasets than the recently introduced Network-Based Stratification method while enabling real-time search. To the best of our knowledge, this is the first attempt to simultaneously characterize and represent somatic mutational data for efficient search purposes.
  
  
  BACKGROUND
  The implementations are available at: https://sites.google.com/site/postechdm/research/implementation/orgos.
  
  
  BACKGROUND
  sael@cs.stonybrook.edu or hwanjoyu@postech.ac.kr
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science and Engineering, POSTECH, Pohang, South Korea and.
authors.forename: Sungchul
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, USA.
authors.forename: Lee
authors.initials: L
authors.lastname: Sael
authors.affiliation: Department of Computer Science and Engineering, POSTECH, Pohang, South Korea and.
authors.forename: Hwanjo
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016009:Chi-Square Distribution; D063990:Gene Ontology; D006801:Humans; D009154:Mutation / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics; D015203:Reproducibility of Results; D016019:Survival Analysis