Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Area of Science:

• Neuroscience
• Bioinformatics
• Genomics
• Proteomics
• Metabolomics

Background:

• Omics data analysis, termed neuroinformatics, is crucial in modern biology.
• Data mining of omics datasets generates hypotheses for neurodegenerative diseases.
• Understanding complex biological regulation requires integrating multi-omics data.

Purpose of the Study:

• To review data mining studies of individual omics datasets for Alzheimer's, Parkinson's, and multiple sclerosis.
• To discuss multi-omics integration approaches for interpreting biological data.
• To assess multi-omics integration in data mining for brain network reconstruction.

Main Methods:

• Summarizing individual omics data mining studies (epigenetics, transcriptomics, proteomics, metabolomics, lipidomics, spatial omics).
• Discussing multi-omics integration methods (independent biological and unsupervised integration).
• Assessing integrated multi-omics data mining for systems bioinformatics.

Main Results:

• Individual omics data mining provides insights into neurodegenerative disease mechanisms.
• Multi-omics integration enhances the interpretation of complex biological data.
• Integrated multi-omics approaches offer proof-of-concept for brain network reconstruction.

Conclusions:

• Combining high-dimensional bioinformatics analysis with experimental validation is recommended for translational neuroscience.
• Integrative multi-omics and systems bioinformatics can achieve precision phenotyping of neurodegenerative diseases.
• Future opportunities lie in personalized medicine for brain disorders.