A Faster cDNA Microarray Gene Expression Data Classifier for Diagnosing Diseases

Sun Yuan Hsieh, Yu Chun Chou

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Profiling cancer molecules has several advantages; however, using microarray technology in routine clinical diagnostics is challenging for physicians. The classification of microarray data has two main limitations: 1) the data set is unreliable for building classifiers; and 2) the classifiers exhibit poor performance. Current microarray classification algorithms typically yield a high rate of false-positives cases, which is unacceptable in diagnostic applications. Numerous algorithms have been developed to detect false-positive cases; however, they require a considerable computation time. To address this problem, this study enhanced a previously proposed gene expression graph (GEG)-based classifier to shorten the computation time. The modified classifier filters genes by using an edge weight to determine their significance, thereby facilitating accurate comparison and classification. This study experimentally compared the proposed classifier with a GEG-based classifier by using real data and benchmark tests. The results show that the proposed classifier is faster at detecting false-positives.

Original languageEnglish
Article number7229303
Pages (from-to)43-54
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
Publication statusPublished - 2016 Jan 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics


Dive into the research topics of 'A Faster cDNA Microarray Gene Expression Data Classifier for Diagnosing Diseases'. Together they form a unique fingerprint.

Cite this