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Multi-omics data integration methods and their applications in psychiatric disorders
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DOI：10.1016/j.euroneuro.2023.01.001.png)
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Abstract
En 中文
combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and ma-chine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treat-ment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current chal-lenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry. (c) 2023 Elsevier B.V. and ECNP. All rights reserved.
Keywords:
Multi-omics
Genomics
Transcriptomics
Statistics
Machine learning
Psychiatry
Bench to bedside
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