Proceedings of the 2007 ACM symposium on Applied computing
Taxonomy-based partitioning of the Gene Ontology
Journal of Biomedical Informatics
Ameliorative missing value imputation for robust biological knowledge inference
Journal of Biomedical Informatics
Assessing agreement of clustering methods with gene expression microarray data
Computational Statistics & Data Analysis
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
How to improve postgenomic knowledge discovery using imputation
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on applications of signal procesing techniques to bioinformatics, genomics, and proteomics
International Journal of Data Mining and Bioinformatics
EURASIP Journal on Bioinformatics and Systems Biology
NanoParticle Ontology for cancer nanotechnology research
Journal of Biomedical Informatics
Missing value estimation for DNA microarrays with mutliresolution schemes
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Motivation: 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. Availability: Java and Matlab codes are available on request from the authors. Supplementary material: Available online at http://users.utu.fi/jotatu/GOImpute.html Contact: jotatu@utu.fi