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Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Data analysis methods for defining biomarkers from omics data.

Chao Li^1,2, Zhenbo Gao¹, Benzhe Su¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.

Analytical and Bioanalytical Chemistry

|December 24, 2021

Summary

This summary is machine-generated.

Omics technologies like genomics and proteomics offer new insights into complex diseases. This review covers advanced methods for biomarker discovery from omics data, crucial for diagnosis and prognosis.

Keywords:

Biomarker discovery Combinatorial features Molecular biomarkers Network analysis Omics data analysis

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Area of Science:

Biomedical research
Bioinformatics
Systems biology

Background:

Omics technologies (genomics, epigenomics, transcriptomics, proteomics, metabolomics) have advanced disease diagnosis and prognosis.
Analyzing large, complex omics data is critical for extracting meaningful biological information.
Biomarker discovery from omics data is essential for understanding complex diseases.

Purpose of the Study:

To review recent advances in biomarker discovery methods using omics data.
To categorize these methods into individual, combinatorial, and network analyses.
To discuss current challenges and future perspectives in the field.

Main Methods:

Review of biomarker discovery methods from the last decade.
Categorization based on analysis type: individual feature, combinatorial feature, and network analysis.
Focus on methods applicable to large and complex omics datasets.

Main Results:

Identification of key trends in omics-based biomarker discovery.
Classification of methods enabling comprehensive analysis of omics data.
Highlighting the importance of advanced analytical approaches for biomarker identification.

Conclusions:

Biomarker discovery from omics data is rapidly evolving.
Advanced analytical strategies are crucial for harnessing the potential of omics data.
Continued research is needed to address challenges and explore future directions in the field.