A Faster cDNA Microarray Gene Expression Data Classifier for Diagnosing Diseases

Sun Yuan Hsieh, Yu Chun Chou

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號7229303
頁(從 - 到)43-54
頁數12
期刊IEEE/ACM Transactions on Computational Biology and Bioinformatics
13
發行號1
DOIs
出版狀態Published - 2016 一月 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Genetics
  • Applied Mathematics

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