title: Improving missing value estimation in microarray data with gene ontology.
abstract: BACKGROUND
  Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation.
  
  
  RESULTS
  We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments.
  
  
  BACKGROUND
  Java and Matlab codes are available on request from the authors.
  
  
  BACKGROUND
  Available online at http://users.utu.fi/jotatu/GOImpute.html.
authors.affiliation: Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland. jotatu@utu.fi
authors.forename: Johannes
authors.initials: J
authors.lastname: Tuikkala
authors.forename: Laura
authors.initials: L
authors.lastname: Elo
authors.forename: Olli S
authors.initials: OS
authors.lastname: Nevalainen
authors.forename: Tero
authors.initials: T
authors.lastname: Aittokallio
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012984:Software