The Intersection of Multi-Omics Data and Machine Learning in Medicine

Dear Colleagues,

The integration of multi-omics data and machine learning in medicine has gained significant momentum, revolutionizing our understanding of disease mechanisms and personalized treatment. Multi-omics data analysis, encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics, provides a holistic view of biological systems. Leveraging machine learning algorithms, researchers can extract valuable insights, including disease biomarkers and predictive models, with the potential to transform healthcare outcomes.

The aim is to present the latest advancements, challenges, and prospects in the field of integrating multi-omics data and machine learning techniques in medicine, foster interdisciplinary discussions, and contribute to the advancement of precision medicine and improved patient care. The scope includes research studies, reviews, and methodologies that explore the application of multi-omics data analysis and machine learning algorithms in areas such as disease classification, biomarker identification, predictive modeling, network analysis, medical imaging, and ethical considerations.

Advancements in algorithms and hardware, such as graphics processing unit (GPU) computation, have driven a transformative shift in machine learning (ML). Simultaneously, there has been an exponential increase in the volume of data generated through high-throughput sequencing and other -omics techniques, including genomics and transcriptomics. The developments in Machine learning (ML) and omics data provide powerful tools for understanding diseases, improving diagnostics, and guiding personalized treatment strategies.

Advanced machine learning algorithms are being applied to large-scale multi-omics datasets to identify molecular biomarkers specific to diseases. These biomarkers can aid in the early detection, prognosis, and development of targeted therapies. Machine learning techniques are being employed to build predictive models that can forecast disease outcomes and treatment responses based on multi-omics data. This enables personalized medicine by helping clinicians tailor treatment plans to individual patients.

Original research articles that present novel findings and insights in the integration of multi-omics data and machine learning techniques in medicine; papers that propose new methodologies, algorithms, or computational tools specifically designed for the analysis and interpretation of multi-omics data in the context of medicine; comprehensive review articles that provide a critical overview of the current state of the art in the field of multi-omics data integration and machine learning in medicine; papers that showcase the application of multi-omics data and machine learning in disease diagnostics, treatment selection, and patient management; and papers that discuss the ethical considerations and challenges associated with the use of multi-omics data and machine learning in medicine are all welcome. These papers can explore topics such as privacy concerns, data sharing, bias mitigation, interpretability, and the responsible deployment of these technologies in clinical settings.

Dr. Hui Wu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

•  multi-omics data
•  machine learning
•  precision medicine
•  personalized medicine
•  biomarker
•  genomics
•  transcriptomics
•  proteomics
•  metabolomics
•  epigenomics