HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification

Abstract

The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm of cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. The identification and characterization of cancer-associated ncPEPs assume a crucial role in propelling cancer research forward and augmenting our comprehension of immune-related processes. However, prevailing methods for cancer-associated ncPEPs identification primarily rely on sequence order, neglecting the latent relationships between peptides. As widely acknowledged in the central dogma, peptides are translated from RNA via a many-to-many mapping. In this study, we capitalize on the translation relationship and introduce a hypergraph-based model called HGCPep to enhance the representation of peptide features. Specifically, RNA is regarded as a hyperedge connecting the peptides that are translated from it. Our experimental results validate that the HGCPep approach, leveraging the strengths of hypergraph and convolutional neural networks, outperforms alternative methodologies in the recognition of ncPEPs. Additionally, employing a reduction tool, we visualize the results of multi-label classification within a 2D latent space, shedding light on how multi-label classification task influence the representation of peptides. The dataset and source code of our proposed method can be found via https://github.com/Longwt123/HGCPep_Github.