Platform for Representation and Integration of multimodal Molecular Embeddings

• URL: http://arxiv.org/abs/2507.07367v1
• Date: Thu, 10 Jul 2025 01:18:50 GMT
• Title: Platform for Representation and Integration of multimodal Molecular Embeddings
• Authors: Erika Yilin Zheng, Yu Yan, Baradwaj Simha Sankar, Ethan Ji, Steven Swee, Irsyad Adam, Ding Wang, Alexander Russell Pelletier, Alex Bui, Wei Wang, Peipei Ping,
• Abstract summary: Existing machine learning methods for molecular embeddings are restricted to specific tasks or data modalities.<n>Existing embeddings capture largely non-overlapping molecular signals, highlighting the value of embedding integration.<n>We propose Platform for Representation and Integration of multimodal Molecular Embeddings (PRISME) to integrate heterogeneous embeddings into a unified multimodal representation.
• Score: 43.54912893426355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing machine learning methods for molecular (e.g., gene) embeddings are restricted to specific tasks or data modalities, limiting their effectiveness within narrow domains. As a result, they fail to capture the full breadth of gene functions and interactions across diverse biological contexts. In this study, we have systematically evaluated knowledge representations of biomolecules across multiple dimensions representing a task-agnostic manner spanning three major data sources, including omics experimental data, literature-derived text data, and knowledge graph-based representations. To distinguish between meaningful biological signals from chance correlations, we devised an adjusted variant of Singular Vector Canonical Correlation Analysis (SVCCA) that quantifies signal redundancy and complementarity across different data modalities and sources. These analyses reveal that existing embeddings capture largely non-overlapping molecular signals, highlighting the value of embedding integration. Building on this insight, we propose Platform for Representation and Integration of multimodal Molecular Embeddings (PRISME), a machine learning based workflow using an autoencoder to integrate these heterogeneous embeddings into a unified multimodal representation. We validated this approach across various benchmark tasks, where PRISME demonstrated consistent performance, and outperformed individual embedding methods in missing value imputations. This new framework supports comprehensive modeling of biomolecules, advancing the development of robust, broadly applicable multimodal embeddings optimized for downstream biomedical machine learning applications.

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