Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction

Qiuming Zhu, Hongmei Cui, Kajia Cao, Wing C. Chan

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Many different methods and techniques have been investigated for the processing and analysis of microarray gene expression profiling datasets. It is noted that the accuracy and reliability of the results are often dependent on the measurement approaches applied, and no single measurement so far is guaranteed to generate a satisfactory result. In this paper, an algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling. The approach integrates a set of measurements from different aspects in terms of the discrepancy indications and merit expectations of the gene expression patterns with respect to the clinical outcomes. A combination of statistical and non-statistical criteria, continuous and discrete parameterizations, as well as model-based and modeless evaluations is applied in the approach. By integrating these measurements, a set of genes that are indicative to the clinical outcomes are better captured from the gene expression profiling dataset.

Original languageEnglish (US)
Pages (from-to)79-88
Number of pages10
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number2
StatePublished - Jun 2004


  • Algorithmic fusion
  • Cross-projection (CP)
  • Discrete partition (DP)
  • Fisher's discrimination
  • Gene expression profiling
  • Outcome prediction

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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