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Computer-aided biomarker discovery for precision medicine: data resources, models and applications.
Yuxin Lin1, Fuliang Qian1, Li Shen1
1Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.
Briefings in Bioinformatics
|December 2, 2017
Summary
Computer-aided biomarker discovery leverages big data and bioinformatics for efficient disease prediction. This approach identifies molecular and network biomarkers for improved diagnosis, prognosis, and therapy, advancing precision medicine.
Area of Science:
- Biomedical Science
- Bioinformatics
- Systems Biology
Background:
- Biomarkers are crucial indicators for predicting disease initiation and progression.
- Biomarkers capture dynamic biological state changes, offering advantages over static disease factors.
- Advancements in computational methods and big data analysis have propelled computer-aided biomarker discovery.
Purpose of the Study:
- To review the concept and characteristics of various biomarker types, including single molecular, module/network, and cross-level biomarkers.
- To introduce publicly available data resources, biomarker databases, and knowledge bases relevant to biomarker discovery.
- To discuss computational models for biomarker identification, highlighting a novel bioinformatics model for microRNA biomarker discovery.
Main Methods:
- Systems biology principles guide the explication of biomarker types.
- Review of mathematical, network, and machine learning theories applied to biomarker identification.
- Highlighting a novel bioinformatics model utilizing network substructural and functional evidence for microRNA biomarker discovery.
Main Results:
- Bioinformatics approaches offer a more efficient and holistic framework for decoding disease pathogenesis compared to traditional wet-lab experiments.
- Computational methods facilitate the identification of diverse biomarkers, from single molecules to complex molecular networks.
- The review provides insights into the advantages and challenges of current computational biomarker detection approaches.
Conclusions:
- Computer-aided biomarker discovery is a burgeoning paradigm in biomedical science, driven by big data and computational advancements.
- This approach holds significant promise for disease diagnosis, prognosis, and therapy, contributing to precision medicine and healthcare.
- Future research should focus on refining computational strategies for more effective biomarker detection and application.
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