Using Gene Ontology to Generate Biological Contexts for Augmenting Statistical Patterns of Molecular Data

摘要

It is widely acknowledged in the bioinformatics community that many of the available datasets used for discovering possible molecular diagnostic patterns have very low case-to-feature ratios and this leads to problems of high false positive discovery rates. We (and others) contend that these ratios are so low that the usual methods of cross-validation, re-sampling, etc. will never eliminate the problem. We conjecture that additional knowledge sources are needed to augment the statistical pattern discovery. We describe our initial attempts to do this by combining information from the Gene Ontology with multiple sets of discriminating patterns discovered in a publicly available gene expression dataset on breast cancer. Using a second validation dataset, we identify some "good" and "poor" validators among the candidate patterns and then show that certain biological contexts appear to provide some ability to discriminate between them. We also describe some approaches we took to dealing with the non-uniformity in online biological data banks and the difficulties this creates when seeking information on a large number of genes. While we do not claim to have proven the value of this approach we are encouraged by these initial findings.