In recent years, the disciplines of omic science and network medicine have become increasingly important to the study of human pathobiology. Network-based approaches have potential biological and clinical applications through the identification of disease genes, and advances in statistical methods for data processing has revolutionized modern medicine by providing more precise diagnoses.
By the principle of network medicine, human disease phenotype is rarely a consequence of an abnormality in a single effector gene product but reflects various pathobiological processes that interact in a complex network. In order to decipher the molecular interactions underlying complex human diseases, computation approaches are required which encompass multi-layers data integration, genome-wide studies (epigenomics, genomics and proteomics) and data analysis including machine learning algorithms. These are integral for precision medicine as they are used to translate research findings into novel prognostic factors, non-invasive biomarkers, and drug targets.
The present Research Topic aims to investigate new avenues of experimental and computational approaches covering, but not limited to:
• omics or multi-omic studies, including radiomics
• regulatory network, including host-pathogen interaction networks
• machine learning applications
We encourage mainly Original Articles and Reviews, which provide additional insight and significant advances in human complex diseases such as cardiovascular diseases, oncology, nervous system disorders, metabolism-related diseases.
Manuscripts within this collection are expected to cover robust bioinformatics workflow, use of the existing or novel mathematical models for risk stratification, prognostication, network-based approach for identifying disease modules, biomarkers, or druggable targets.
Manuscript without external validations on additional datasets and/or experimental validations, will not be accepted. Frontiers reserve the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
In recent years, the disciplines of omic science and network medicine have become increasingly important to the study of human pathobiology. Network-based approaches have potential biological and clinical applications through the identification of disease genes, and advances in statistical methods for data processing has revolutionized modern medicine by providing more precise diagnoses.
By the principle of network medicine, human disease phenotype is rarely a consequence of an abnormality in a single effector gene product but reflects various pathobiological processes that interact in a complex network. In order to decipher the molecular interactions underlying complex human diseases, computation approaches are required which encompass multi-layers data integration, genome-wide studies (epigenomics, genomics and proteomics) and data analysis including machine learning algorithms. These are integral for precision medicine as they are used to translate research findings into novel prognostic factors, non-invasive biomarkers, and drug targets.
The present Research Topic aims to investigate new avenues of experimental and computational approaches covering, but not limited to:
• omics or multi-omic studies, including radiomics
• regulatory network, including host-pathogen interaction networks
• machine learning applications
We encourage mainly Original Articles and Reviews, which provide additional insight and significant advances in human complex diseases such as cardiovascular diseases, oncology, nervous system disorders, metabolism-related diseases.
Manuscripts within this collection are expected to cover robust bioinformatics workflow, use of the existing or novel mathematical models for risk stratification, prognostication, network-based approach for identifying disease modules, biomarkers, or druggable targets.
Manuscript without external validations on additional datasets and/or experimental validations, will not be accepted. Frontiers reserve the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
