Dependence model and network for biomarker identification and cancer classification

Peng Qiu, Z. Jane Wang, K. J.Ray Liu

Research output: Contribution to journalConference articlepeer-review

Abstract

Of particular interest in this paper is to develop statistical and modeling approaches for protein biomarker discovery to provide new insights into the early detection and diagnosis of cancer, based on mass spectrometry (MS) data. In this paper, we propose to employ an ensemble dependence model (EDM)-based framework for cancer classification, protein dependence network reconstruction, and further for biomarker identification. The dependency revealed by the EDM reflects the functional relationships between MS peaks and thus provides some insights into the underlying cancer development mechanism. The EDM-based classification scheme is applied to real cancer MS datasets, and provides superior performance for cancer classification when compared with the popular Support Vector Machine algorithm. From the eigenvalue pattern of the dependence model, the dependence networks are constructed to identify cancer biomarkers. Furthermore, for the purpose of comparison, a classification-performance-based biomarker identification criterion is examined. The dependence-network-based biomarkers show much greater consistency in cross validation. Therefore, the proposed dependence-network-based scheme is promising for use as a cancer diagnostic classifier and predictor.

Original languageEnglish (US)
JournalEuropean Signal Processing Conference
StatePublished - 2006
Event14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
Duration: Sep 4 2006Sep 8 2006

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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