Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine, volume II

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103

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Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine, volume II

134.5K

views

103

authors

31

articles

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This Research Topic is Volume II of a series. The previous volume can be found here: Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine.

The field of machine learning has seen remarkable progress, particularly in areas such as computer vision and natural language processing, and has been increasingly applied to bioinformatics and medical image analysis. Despite these advancements, a purely data-driven approach may not fully address the complexities inherent in medical problems. Traditional medical knowledge, characterized by symbolic representation and expert reasoning, offers a wealth of insights that can enhance machine learning algorithms. The integration of this knowledge with machine learning, especially through large-scale models like ChatGPT, has shown promise in improving the performance, interpretability, and reliability of these algorithms. This integration represents a novel research paradigm that could significantly advance personalized medicine by leveraging deep knowledge representation and expert-level reasoning. However, there remains a need for further exploration into how these technologies can be effectively combined to address the unique challenges of individualized medicine.

This Research Topic aims to deepen our understanding of how knowledge-embedded machine learning can address challenges in individualized medicine. We seek to explore advancements in both traditional and large-scale model approaches, particularly in genomic and medical image analysis. The objective is to investigate how these approaches can be optimized to enhance the precision and effectiveness of personalized medical treatments. By focusing on the integration of disease knowledge with machine learning, this research aims to test hypotheses related to the improvement of machine learning applications in personalized medicine, ultimately contributing to more effective and tailored healthcare solutions.

To gather further insights in the integration of medical knowledge with machine learning technologies, we welcome but not limited to, the following themes:

- Development and application of machine learning methods in cancer genomics, utilizing both large-scale models and traditional strategies for cancer risk evaluation.
- Enhancement of image analysis in medical fields through machine learning, exploring both established techniques and novel applications with large-scale models for diagnosis and risk evaluation.
- Innovative work in multi-omics analysis or integrated genomic and image data approaches, focusing on methods that incorporate large-scale machine learning insights for improved integration or interpretability.
- Novel contributions to translational medicine tools and software, including the use of large-scale machine learning for processing high-throughput sequencing data and analyzing radiological and histological images.

This Research Topic is Volume II of a series. The previous volume can be found here: Medical Knowledge-Assisted Machine Learning Technologies in Individualized Medicine.

The field of machine learning has seen remarkable progress, particularly in areas such as computer vision and natural language processing, and has been increasingly applied to bioinformatics and medical image analysis. Despite these advancements, a purely data-driven approach may not fully address the complexities inherent in medical problems. Traditional medical knowledge, characterized by symbolic representation and expert reasoning, offers a wealth of insights that can enhance machine learning algorithms. The integration of this knowledge with machine learning, especially through large-scale models like ChatGPT, has shown promise in improving the performance, interpretability, and reliability of these algorithms. This integration represents a novel research paradigm that could significantly advance personalized medicine by leveraging deep knowledge representation and expert-level reasoning. However, there remains a need for further exploration into how these technologies can be effectively combined to address the unique challenges of individualized medicine.

This Research Topic aims to deepen our understanding of how knowledge-embedded machine learning can address challenges in individualized medicine. We seek to explore advancements in both traditional and large-scale model approaches, particularly in genomic and medical image analysis. The objective is to investigate how these approaches can be optimized to enhance the precision and effectiveness of personalized medical treatments. By focusing on the integration of disease knowledge with machine learning, this research aims to test hypotheses related to the improvement of machine learning applications in personalized medicine, ultimately contributing to more effective and tailored healthcare solutions.

To gather further insights in the integration of medical knowledge with machine learning technologies, we welcome but not limited to, the following themes:

- Development and application of machine learning methods in cancer genomics, utilizing both large-scale models and traditional strategies for cancer risk evaluation.
- Enhancement of image analysis in medical fields through machine learning, exploring both established techniques and novel applications with large-scale models for diagnosis and risk evaluation.
- Innovative work in multi-omics analysis or integrated genomic and image data approaches, focusing on methods that incorporate large-scale machine learning insights for improved integration or interpretability.
- Novel contributions to translational medicine tools and software, including the use of large-scale machine learning for processing high-throughput sequencing data and analyzing radiological and histological images.

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Frontiers in Molecular Biosciences

Molecular Diagnostics and Therapeutics

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Frontiers in Physiology

Computational Physiology and Medicine

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Frontiers in Genetics

Computational Genomics

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