About this Research Topic
In the last few decades, we have witnessed the rapid emergence of big data-driven analytics, a revolution that reshaped the whole healthcare industry. However, an important challenge of health-related big data is how to use complex information networks to provide useful clinical information. Recently, big data research has enabled rapid advancement in the clinical practice for otorhinolaryngology – head and neck Diseases (ORL-HNDs). More specifically, large cohort studies have been established, which serves as the foundation for studying diverse populations and key demographic subgroups, exposures, rare genotypes, as well as gene-environment interactions. Besides, the application of artificial intelligence (AI) and machine learning (ML) greatly facilitates the improvement of clinical diagnosis and management of ORL-HNDs. Meanwhile, the development of multi-omics analysis including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, makes substantial contributions to understanding the underlying mechanisms of the initiation and progression of ORL-HNDs as well as identifying novel drug targets. Therefore, understanding and managing the power of big data, as well as acquiring intelligence from information, is key to solving future health-related issues in ORL-HNDs.
In this research topic, we aim to provide an overview of recent advancement in big data research contributing to the understanding of the genetics basis and the underlying mechanisms of disease initiation and progression, the identification of large cohort based novel risk and prognostic factors, as well as the development and validation of novel diagnostic and therapeutic approaches in otorhinolaryngology – head and neck Diseases, that would provide innovative solutions to the existing challenges of clinical practice.
This Research Topic welcomes the highest quality research related to big data research in all areas of the specialty: otology, audiology, laryngology and phoniatrics, rhinology and skull base, as well as head and neck cancer/surgery. Areas of interest include, but not limited to:
• Artificial intelligence and machine learning in radiological and pathological imaging
• Novel biomarkers for diagnosis and prognosis based on large cohort study
• The development and validation of novel diagnostic and therapeutic approaches based on large cohort study
• Computer aided drug design
• Multi-omics analysis including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics
In this research topic, we aim to provide an overview of recent advancement in big data research contributing to the understanding of the genetics basis and the underlying mechanisms of disease initiation and progression, the identification of large cohort based novel risk and prognostic factors, as well as the development and validation of novel diagnostic and therapeutic approaches in otorhinolaryngology – head and neck Diseases, that would provide innovative solutions to the existing challenges of clinical practice.
This Research Topic welcomes the highest quality research related to big data research in all areas of the specialty: otology, audiology, laryngology and phoniatrics, rhinology and skull base, as well as head and neck cancer/surgery. Areas of interest include, but not limited to:
• Artificial intelligence and machine learning in radiological and pathological imaging
• Novel biomarkers for diagnosis and prognosis based on large cohort study
• The development and validation of novel diagnostic and therapeutic approaches based on large cohort study
• Computer aided drug design
• Multi-omics analysis including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics
Keywords: Big data, Otorhinolaryngology – Head and Neck Diseases, artificial intelligence, cohort study, multi-omics analysis
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.
