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Review
. 2017 Mar 9;8(1):51-60.
doi: 10.1007/s13167-017-0083-9. eCollection 2017 Mar.

Pattern recognition for predictive, preventive, and personalized medicine in cancer

Tingting Cheng  1   2   3 , Xianquan Zhan  1   2   3   4
Affiliations
Review

Pattern recognition for predictive, preventive, and personalized medicine in cancer

Tingting Cheng et al. EPMA J. .

Abstract

Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multi-parameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.

Keywords: Genomics; Metabolomics; Pattern recognition; Peptidomics; Predictive preventive personalized medicine; Proteomics; Radiomics; Systems biology; Transcriptomics.

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Figures

Fig. 1
Fig. 1
Types of biomarkers for cancer
Fig. 2
Fig. 2
Integrative pattern based on omics data
Fig. 3
Fig. 3
Ideal model about pattern recognition
See this image and copyright information in PMC

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