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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17726 |
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| _version_ | 1866914957932625920 |
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| author | Vecchietti, Luiz Felipe Lee, Minji Hangeldiyev, Begench Jung, Hyunkyu Park, Hahnbeom Kim, Tae-Kyun Cha, Meeyoung Kim, Ho Min |
| author_facet | Vecchietti, Luiz Felipe Lee, Minji Hangeldiyev, Begench Jung, Hyunkyu Park, Hahnbeom Kim, Tae-Kyun Cha, Meeyoung Kim, Ho Min |
| contents | Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent advances in interpretable machine learning using structure-based protein representations Vecchietti, Luiz Felipe Lee, Minji Hangeldiyev, Begench Jung, Hyunkyu Park, Hahnbeom Kim, Tae-Kyun Cha, Meeyoung Kim, Ho Min Machine Learning Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design. |
| title | Recent advances in interpretable machine learning using structure-based protein representations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.17726 |