About this Research Topic
Within the rapidly evolving realm of genetics, the incorporation of artificial intelligence (AI) has given way to pioneering perspectives in understanding therapeutic approaches and evolutionary processes. Traditional genetic analyses often find themselves restricted in handling large datasets and prone to human error, due to the intricate nature of genetic information interpretation. AI, particularly machine learning, has demonstrated tremendous potential in processing large-scale genetic data and revealing intricate biological networks. In-depth analysis of genetic data will significantly propel innovation within the biomarker discovery and therapeutic strategies.
This topic will embrace interdisciplinary research, encompassing areas such as bioinformatics, statistics, machine learning, and high-throughput genetic experiments. Through these studies, we aim to establish and validate the development of efficient algorithms for the interpretation of complex genomic data. Furthermore, we aspire to construct predictive models for disease prognosis and treatment responses, amalgamating lab results with clinical data. We hope to conduct multicenter clinical studies to substantiate the practicality and reliability of these models in real-world scenarios. This will provide more effective biomarkers with superior accuracy and better biological relevance for precision medicine, bolstering the translation of precision medicine from the laboratory to the clinic.
We welcome original research articles, reviews, systematic reviews/meta-analyses, perspectives, and opinion pieces related to the theme "Genetic Horizons: Exploring genetic biomarkers in Therapy and Evolution with the Aid of Artificial Intelligence." By addressing these themes and article types, the research topic endeavors to offer groundbreaking insights into the biomarker discovery and therapeutic strategies, laying the groundwork for cutting-edge treatment strategies and deepening our comprehension of precision medicine.
The objective of this theme is to utilize AI technologies in revealing the profound correlations between genetic biomarkers and therapeutic strategies, exploring the untapped potential of genetic biomarkers. The call for papers will focus on multiple directions, including but not limited to:
• The application of AI in the analysis of genetic biomarkers.
• The role of machine learning models in searching for genetic biomarkers.
• The use of multimodal machine learning methods in evaluating the efficacy of genetic biomarkers.
• The discovery of genetic biomarkers based on machine learning.
• The application of machine learning methods integrating multi-omics and genetic biomarker exploration.
This topic will embrace interdisciplinary research, encompassing areas such as bioinformatics, statistics, machine learning, and high-throughput genetic experiments. Through these studies, we aim to establish and validate the development of efficient algorithms for the interpretation of complex genomic data. Furthermore, we aspire to construct predictive models for disease prognosis and treatment responses, amalgamating lab results with clinical data. We hope to conduct multicenter clinical studies to substantiate the practicality and reliability of these models in real-world scenarios. This will provide more effective biomarkers with superior accuracy and better biological relevance for precision medicine, bolstering the translation of precision medicine from the laboratory to the clinic.
We welcome original research articles, reviews, systematic reviews/meta-analyses, perspectives, and opinion pieces related to the theme "Genetic Horizons: Exploring genetic biomarkers in Therapy and Evolution with the Aid of Artificial Intelligence." By addressing these themes and article types, the research topic endeavors to offer groundbreaking insights into the biomarker discovery and therapeutic strategies, laying the groundwork for cutting-edge treatment strategies and deepening our comprehension of precision medicine.
The objective of this theme is to utilize AI technologies in revealing the profound correlations between genetic biomarkers and therapeutic strategies, exploring the untapped potential of genetic biomarkers. The call for papers will focus on multiple directions, including but not limited to:
• The application of AI in the analysis of genetic biomarkers.
• The role of machine learning models in searching for genetic biomarkers.
• The use of multimodal machine learning methods in evaluating the efficacy of genetic biomarkers.
• The discovery of genetic biomarkers based on machine learning.
• The application of machine learning methods integrating multi-omics and genetic biomarker exploration.
Keywords: Genetic biomarker, artificial intelligence, multi-omics data processing
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.
