Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting

作     者：Ünsal, Serbulent Özdemir, Sinem Kasap, Bünyamin Kalaycı, M. Erşan Turhan, Kemal Doğan, Tunca Acar, Aybar C. 

作者机构：Department of Biostatistics and Medical Informatics Faculty of Medicine Graduate School of Health Sciences Karadeniz Technical University Trabzon Turkey  Graduate School of Informatics Middle East Technical University Ankara Turkey Health Sciences University Trabzon Kanuni Training and Research Hospital Medical Microbiology Laboratory Trabzon Turkey Biological Data Science Lab Dept. of Computer Engineering Department of Computer Engineering Hacettepe University Ankara Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Adversarial machine learning 

摘      要：In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and while deep learning models excel with abundant data, they also face difficulties when data is scarce. HOPER addresses this issue by integrating three distinct modalities—protein sequences, biomedical text, and protein-protein interaction (PPI) networks—to create a comprehensive protein representation. The model utilizes autoencoders to generate holistic embeddings, which are then employed for PFP tasks using transfer learning. HOPER outperforms existing methods on a benchmark dataset across all Gene Ontology categories, i.e., molecular function, biological process, and cellular component. Additionally, we demonstrate its practical utility by identifying new immune-escape proteins in lung adenocarcinoma, offering insights into potential therapeutic targets. Our results highlight the effectiveness of multimodal representation learning for overcoming data limitations in biological research, potentially enabling more accurate and scalable protein function prediction. HOPER source code and datasets are available at https://***/kansil/HOPER © 2024, CC BY-NC-ND.