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Pattern recognition for predictive, preventive, and personalized medicine in cancer
Tingting Cheng1,2,3, Xianquan Zhan1,2,3,4
1Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People's Republic of China.
The EPMA Journal
|June 17, 2017
Summary
Predictive, preventive, and personalized medicine (PPPM) requires multi-parameter strategies. Pattern recognition in omics data can identify key molecular panels for accurate cancer PPPM.
Area of Science:
- Oncology
- Bioinformatics
- Systems Biology
Background:
- Cancer is a complex disease driven by multi-factorial molecular alterations across genomic, transcriptomic, proteomic, metabolomic, and radiomic levels.
- The intricate network of these alterations makes single-molecule biomarkers insufficient for personalized cancer care.
- Predictive, Preventive, and Personalized Medicine (PPPM) is crucial for advancing cancer treatment and management.
Purpose of the Study:
- To review the pathophysiological basis and methodologies of pattern recognition for PPPM in cancer.
- To highlight the potential of modern omics, computational biology, and systems biology in identifying reliable molecular patterns.
- To translate the concept of multi-parameter strategies into tangible research and development for PPPM and precision medicine (PM) in oncology.
Main Methods:
- Review of existing literature on pattern recognition in cancer research.
- Analysis of omics, computational biology, and systems biology approaches.
- Discussion of the integration of multi-parameter data for biomarker discovery.
Main Results:
- Pattern recognition is an effective methodology for discovering key molecular panels necessary for PPPM.
- Modern technologies enable the recognition of reliable molecular patterns for PPPM in cancer.
- Multi-parameter strategies are essential for accurate cancer prediction, prevention, diagnosis, and treatment.
Conclusions:
- Pattern recognition, powered by omics and systems biology, is vital for advancing PPPM in cancer.
- The development of key molecule-panels through pattern recognition will facilitate personalized cancer care.
- This review supports the translation of multi-parameter strategies into real-world PPPM and precision medicine applications for cancer.
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