Scholarlink

In order to begin to decipher the structure of the cell, we need to integrate multiple types of data of different scales on subcellular organization. Such integration requires dealing with multiple data modalities and with missing data. To this end, we developed MIRAGE, a multi-modal generative model for integrating protein sequence, protein-protein interaction, and protein localization data. Our approach successfully learns a joint embedding space that captures the complex relationships between these diverse modalities. We evaluate our model’s performance against existing methods, obtaining superior performance in several key tasks, including protein function prediction and module detection. MIRAGE source code is available at https://github.com/raminass/MIRAGE . A full manuscript describing MIRAGE and its applications is available at https://www.biorxiv.org/content/10.1101/2025.01.16.633332 .

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Adversarial Scheme for Integrating Multi-modal Data on Protein Function

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Rami Nasser,
  • Leah V. Schaffer,
  • Trey Ideker,
  • Roded Sharan

摘要

In order to begin to decipher the structure of the cell, we need to integrate multiple types of data of different scales on subcellular organization. Such integration requires dealing with multiple data modalities and with missing data. To this end, we developed MIRAGE, a multi-modal generative model for integrating protein sequence, protein-protein interaction, and protein localization data. Our approach successfully learns a joint embedding space that captures the complex relationships between these diverse modalities. We evaluate our model’s performance against existing methods, obtaining superior performance in several key tasks, including protein function prediction and module detection. MIRAGE source code is available at https://github.com/raminass/MIRAGE . A full manuscript describing MIRAGE and its applications is available at https://www.biorxiv.org/content/10.1101/2025.01.16.633332 .