Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer

Masaru Ushijima, Satoshi Miyata, Shinto Eguchi, Masanori Kawakita, Masataka Yoshimoto, Takuji Iwase, Futoshi Akiyama, Goi Sakamoto, Koichi Nagasaki, Yoshio Miki, Tetsuo Noda, Yutaka Hoshikawa, Masaaki Matsuura

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We propose a method for biomarker discovery from mass spectrometry data, improving the common peak approach developed by Fushiki et al. (BMC Bioinformatics, 7:358, 2006). The common peak method is a simple way to select the sensible peaks that are shared with many subjects among all detected peaks by combining a standard spectrum alignment and kernel density estimates. The key idea of our proposed method is to apply the common peak approach to each class label separately. Hence, the proposed method gains more informative peaks for predicting class labels, while minor peaks associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set from laser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for pattern recognition, based on the set of candidate peaks selected by the proposed method. The analysis gives good performance in the sense of test errors for classifying the class labels for a given feature vector of selected peak values.

Original languageEnglish
Pages (from-to)285-293
Number of pages9
JournalCancer Informatics
Volume3
Publication statusPublished - 2007 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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  • Cite this

    Ushijima, M., Miyata, S., Eguchi, S., Kawakita, M., Yoshimoto, M., Iwase, T., Akiyama, F., Sakamoto, G., Nagasaki, K., Miki, Y., Noda, T., Hoshikawa, Y., & Matsuura, M. (2007). Common peak approach using mass spectrometry data sets for predicting the effects of anticancer drugs on breast cancer. Cancer Informatics, 3, 285-293.