Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Gene expression is regulated by complex molecular interactions.
• Analyzing multi-omics data is crucial for understanding patient-specific gene regulation.
• Current methods face challenges in data integration, computational demands, and result interpretation.

Purpose of the Study:

• To survey machine learning (ML) methods for gene regulation studies.
• To explore the utility of cloud computing for multi-omics data analysis.
• To review existing cloud systems for biological data analysis.

Main Methods:

• Categorized ML methods for gene regulation based on five key goals: subnetwork discovery, disease subtype analysis, survival analysis, clinical prediction, and visualization.
• Summarized ML methods by multi-omics input types.
• Reviewed cloud systems (Galaxy, BioVLAB) for multi-omics data analysis.

Main Results:

• Identified various ML approaches applicable to gene regulation.
• Highlighted the advantages of cloud computing for handling large-scale multi-omics data.
• Provided an overview of current cloud-based bioinformatics platforms.

Conclusions:

• Machine learning methods, when deployed on the cloud, can effectively address challenges in multi-omics data analysis for gene regulation studies.
• Cloud platforms offer scalable infrastructure and facilitate collaborative research in genomics.
• Further discussion on cloud implementation issues is needed for optimal utilization.