Integrated Diagnostics and Biomarker Discovery in Endocrinology and Biomedical Sciences

32.8K

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116

authors

17

articles

Integrated Diagnostics and Biomarker Discovery in Endocrinology and Biomedical Sciences

32.8K

views

116

authors

17

articles

Editors

3

National Institutes of Health (NIH)

Impact

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Recent advancements and cost reduction in various omics technologies have provided us with a wealth of data at the molecular, cellular and organ levels. Other types of comprehensive data sets are also increasingly available at the individual, community and population levels. The integration of these different types of data are enabling multidisciplinary convergences in diagnostics and biomarker discovery in various biomedical fields to realize a more holistic, systems biology view of diseases. Such integrated approaches have also proven useful in the field of endocrinology, particularly in better understanding complex, heterogeneous diseases such as type 2 diabetes mellitus. For instance, researchers have combined genotyping data and RNA-sequencing data with glycomics and lipidomics to identify potential biomarkers in type 2 diabetes.

Here, we propose a special issue to foster various types of integrative efforts in endocrinology to identify prognostic/diagnostic markers and better understand disease mechanisms. The subject matter will, however, not be limited to multi-omics, but may include topics such as multi-modal single-cell applications, spatial genomics, imaging, digital twin technology integrated with electronic medical records, integration of metaverse with biomedical research for visualization, companion diagnostics, digital therapeutics and precision approaches. This special issue will aid in catalyzing integrative and interdisciplinary research in endocrinology by expanding systems-level understanding of complex diseases.

COI: Dr. Yun and Dr. Francis are funded by Predictiv Care, Inc.

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National Institutes of Health (NIH)

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Published In

Frontiers in Endocrinology

Systems Endocrinology

Frontiers in Genetics

Systems Endocrinology

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