Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.
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| Main Authors: | , , |
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| Format: | Artículo científico |
| Language: | en |
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FEBS open bio
2025
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| _version_ | 1868266239815581696 |
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| author | Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi |
| author_facet | Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi |
| collection | PubMed - marine biology |
| contents | Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine. Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi Precision Medicine Humans Machine Learning Genomics Computational Biology Multiomics Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes of individuals to enable personalized medicine. With the increasing complexity of omics data, particularly multiomics, there is a growing need for advanced computational frameworks to interpret these data effectively. Foundation models (FMs), large-scale machine learning models pretrained on diverse data types, have recently emerged as powerful tools for improving data interpretability and decision-making in precision medicine. This review discusses the integration of FMs into MDT systems, particularly their role in enhancing the interpretability of multiomics data. We examine current challenges, recent advancements, and future opportunities in leveraging FMs for multiomics analysis in MDTs, with a focus on their application in precision medicine. |
| format | Artículo científico |
| id | pubmed_39995150 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | FEBS open bio |
| record_format | pubmed |
| spellingShingle | Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine. Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi Precision Medicine Humans Machine Learning Genomics Computational Biology Multiomics Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine. Alsaedi, Sakhaa Gao, Xin Gojobori, Takashi Precision Medicine Humans Machine Learning Genomics Computational Biology Multiomics Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes of individuals to enable personalized medicine. With the increasing complexity of omics data, particularly multiomics, there is a growing need for advanced computational frameworks to interpret these data effectively. Foundation models (FMs), large-scale machine learning models pretrained on diverse data types, have recently emerged as powerful tools for improving data interpretability and decision-making in precision medicine. This review discusses the integration of FMs into MDT systems, particularly their role in enhancing the interpretability of multiomics data. We examine current challenges, recent advancements, and future opportunities in leveraging FMs for multiomics analysis in MDTs, with a focus on their application in precision medicine. |
| title | Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine. |
| topic | Precision Medicine Humans Machine Learning Genomics Computational Biology Multiomics |
| url | https://pubmed.ncbi.nlm.nih.gov/39995150/ |