Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting
Journal:
arXiv
Published Date:
Nov 22, 2024
Abstract
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://github.com/kansil/HOPER
