Binary (0,1) matrices, commonly known as transactional databases, can represent many application data, including gene-phenotype data where "1 represents a confirmed gene-phenotype relation and "0 represents an unknown relation. It is natural to ask what information is hidden behind these "0s and "1s. Unfortunately, recent matrix completion methods, though very effective in many cases, are less likely to infer something interesting from these (0,1)-matrices. To answer this challenge, we propose Ind Evi, a very succinct and effective algorithm to perform independent-evidence-based transactional database transformation. Each entry of a (0,1)-matrix is evaluated by "independent evidence (maximal supporting patterns) extracted from the whole matrix for this entry. The value of an entry, regardless of its value as 0 or 1, has completely no effect for its independent evidence. The experiment on a gene-phenotype database shows that our method is highly promising in ranking candidate genes and predicting unknown disease genes.

Original languageEnglish
Article number5740841
Pages (from-to)294-304
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
StatePublished - 2012


  • Transactional database
  • binary matrix
  • disease gene
  • frequent item set mining
  • matrix completion
  • maximal biclique
  • phenotype
  • prioritization


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