Platform for Representation and Integration of multimodal Molecular Embeddings

2025-07-09

AI Summary

Existing gene embedding methods are typically confined to single tasks or modalities, limiting their ability to comprehensively capture the biological diversity of gene functions and interactions. To address this, we propose PRISME—a unified multimodal gene representation framework that systematically integrates three heterogeneous data sources: omics experimental measurements, biomedical literature text, and structured knowledge graphs. PRISME introduces an enhanced Singular Vector Canonical Correlation Analysis (SVCCA) to quantitatively assess inter-modal redundancy and complementarity, thereby guiding autoencoder-based cross-modal fusion. Evaluated on multiple benchmark tasks—including missing value imputation and gene function prediction—PRISME consistently outperforms unimodal baselines and significantly enhances downstream biomedical model performance. By enabling interpretable, generalizable molecular intelligence, PRISME establishes a novel paradigm for integrative, multimodal representation learning in computational biology.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Existing molecular embeddings are limited to specific tasks or data modalities
• Current methods fail to capture full gene functions across diverse biological contexts
• Lack of a unified framework for integrating multimodal molecular embeddings

Innovation

• Adjusted SVCCA for signal redundancy analysis
• Autoencoder integrates heterogeneous molecular embeddings
• PRISME unifies multimodal biomolecular representations