About this Research Topic
Human diseases are an unavoidable part of the life process, posing significant threats to human health. With the development of high-throughput sequencing experimental technologies, an amount of genomics, transcriptomics, proteomics, metabolomics, and more omics data has been generated. These diverse data types hold great potential for revealing the molecular mechanisms, identifying novel biomarkers, and developing personalized treatment strategies. However, the effective integration and analysis of these multi-omics data, characterized by high heterogeneity and dimensionality, remain a formidable challenge.
Traditionally, analyzing multi-omics data to investigate disease development through biological experiments has been time-consuming and resource-intensive. In recent years, computational methods have emerged as promising approaches in bioinformatics, such as network analysis, machine learning, and deep learning. These advanced computational techniques enable researchers to extract more meaningful biological insights from multi-omics data, facilitating early diagnosis, precise patient stratification, and personalized treatment approaches.
This special issue aims to showcase the latest research advances and innovative methods in multi-omics data integration and analysis, exploring their applications in disease diagnosis and treatment. By providing a platform for sharing state-of-the-art research, we hope to foster collaboration among researchers in this field and accelerate the development of multi-omics data-driven precision medicine.
The aim of this research topic is to explore and evaluate advanced computational methods for integrating multi-omics data in disease diagnosis and treatment. Our goal is to address key challenges in data integration, feature selection, model construction, and biological interpretation. To this end, we encourage researchers to propose innovative algorithms, develop novel software tools, and demonstrate the potential of these methods in practical clinical applications. We are particularly interested in approaches that can improve the accuracy of disease diagnosis, predict treatment responses, and guide personalized treatment strategies.
Topics of interest include, but are no limited to:
• Computational methods and algorithms for multi-omics data integration
• Machine learning and deep learning applications for multi-omics data analysis
• Multi-omics data-driven classification of disease subtypes
• Computational methods for biomarker discovery through multi-omics data integration
Traditionally, analyzing multi-omics data to investigate disease development through biological experiments has been time-consuming and resource-intensive. In recent years, computational methods have emerged as promising approaches in bioinformatics, such as network analysis, machine learning, and deep learning. These advanced computational techniques enable researchers to extract more meaningful biological insights from multi-omics data, facilitating early diagnosis, precise patient stratification, and personalized treatment approaches.
This special issue aims to showcase the latest research advances and innovative methods in multi-omics data integration and analysis, exploring their applications in disease diagnosis and treatment. By providing a platform for sharing state-of-the-art research, we hope to foster collaboration among researchers in this field and accelerate the development of multi-omics data-driven precision medicine.
The aim of this research topic is to explore and evaluate advanced computational methods for integrating multi-omics data in disease diagnosis and treatment. Our goal is to address key challenges in data integration, feature selection, model construction, and biological interpretation. To this end, we encourage researchers to propose innovative algorithms, develop novel software tools, and demonstrate the potential of these methods in practical clinical applications. We are particularly interested in approaches that can improve the accuracy of disease diagnosis, predict treatment responses, and guide personalized treatment strategies.
Topics of interest include, but are no limited to:
• Computational methods and algorithms for multi-omics data integration
• Machine learning and deep learning applications for multi-omics data analysis
• Multi-omics data-driven classification of disease subtypes
• Computational methods for biomarker discovery through multi-omics data integration
Keywords: machine learning, deep learning, multi-omics data, computational method, disease biomarker
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
