Enhancing mental health therapy with multi-omics and machine learning

Mental disorders, affecting millions worldwide, present a significant challenge in clinical practice due to their complex etiology and varied manifestations. Traditional therapeutic approaches often fail to provide personalized care, underscoring the need for more innovative strategies. Multi-omics technologies, including genomics, proteomics, and metabolomics, allow for a broader understanding of biological processes. When these technologies are used in conjunction with advanced machine learning (ML) techniques such as generative AI and gradient boosting, there is potential to improve the specificity and effectiveness of treatments. This combination aims to better match therapies to individual patient profiles, potentially enhancing therapeutic outcomes and reducing adverse effects.

The main goal of this Research Topic is to overcome the limitations of current mental health treatments by utilizing multi-omics biomarkers in conjunction with ML. Conventional treatments are often inadequate, leading many practitioners to a trial-and-error approach that can result in suboptimal patient experiences or be accompanied by the development of adverse reactions. By integrating diverse omics data with robust ML techniques, we plan to develop predictive models that can identify biomarkers relevant to various mental health conditions. This collaborative effort aims to establish tailored treatment plans that are clinically validated, thereby facilitating the integration of these advances into routine clinical practice and ultimately improving treatment precision and outcomes.

This Research Topic invites contributions that explore how multi-omics biomarkers and ML can jointly advance the efficacy and safety of mental health therapies. We are especially interested in studies on the following aspects:

o Discovery and validation of biomarkers using multi-omics methods.

o Creation of predictive models through advanced ML techniques.

o Clinical trial evaluations of these models in personalized care.

Submissions may include original research, reviews, and case studies that focus on merging omics data with ML for mental health. Our objective is to gather research that highlights novel methodologies and significant findings, aiding the advancement of precision medicine in mental health care and encouraging further research and clinical developments.

Topic Editor Michael Zastrozhin is the founder and CEO of PGxAI, a company specializing in generative AI in precision medicine. Topic Editor Eric Rytkin is a stockholder of NuSera Biosystems Inc. All other Topic Editors declare no competing interests concerning the Research Topic subject.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review

Keywords: omics, precision medicine, personalized medicine, artificial intelligence, machine learning, generative ai, vector search, gradient boosting, ai, ml

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.