Recent technologies have provided us with an incredible amount of ‘multi-omic’ data, broadening the horizons of the genetic, transcriptomic, and epigenetic information that is available for deciphering the subgroups of patients suffering from certain diseases, thus paving the way for a variety of applications in precision medicine. Among the technologies are sequencing-based approaches including high throughput DNA/RNA sequencing, ATAC-seq, TCR-seq, DNA/RNA methylome-seq, and single-cell sequencing, and image-based technologies including x-ray, CT, PET-CT, MRI, digital pathology, immunohistochemistry, and optical imaging.

In recent years, many computational approaches have gained unprecedented momentum by providing opportunities for using multi-omic datasets that are generated from those technologies above to revolutionize disease research and care. Thus, multi-omic data-based computational approaches are particularly well suited for exploring the underlying mechanisms of biological phenomena, aiming to achieve personalized disease diagnosis, prognosis, and treatment for precision medicine. However, handling and making sense of the increasing amount of information represents one of the biggest challenges of the decade. There is an urgent need to develop new computational and statistical approaches, especially in the field of personalized disease diagnosis, prognosis, and treatment.

The aim of this Special Issue is to provide a collection of multidisciplinary research and review articles addressing the development, improvement, or examples of using methods or algorithms for personalized disease diagnosis, prognosis, and treatment.

Potential topics include but are not limited to the following:

• Computational or experimental approaches to describe the landscape of genetic, transcriptomic, and epigenetic information for diseases
• Computational methods or strategies to predict disease diagnosis, prognosis, and treatment
• Computational methods or strategies for prediction of drug-drug interactions and/or quantification of their dose-effect relationship
• Network-based statistical methods and applications in precision medicine with multi-omic data
• Tools, pipelines, and databases for personalized disease diagnosis, prognosis, and treatment
• Machine learning methods in the computational biology of diseases
• Application of computational biology in the screening of biomarkers using multi-omic data
• Artificial Intelligence (AI)-based algorithms for the identification of markers for diagnosis, prognosis, and treatment
• Computational approaches for supporting clinical decision-making processes
• Patient selection methods