CrossPredGO: A Novel Light-weight Cross-modal Multi-attention Framework for Protein Function Prediction.
IEEE/ACM Trans Comput Biol Bioinform
; PP2024 Jun 06.
Article
en En
| MEDLINE
| ID: mdl-38843056
ABSTRACT
Proteins are represented in various ways, each contributing differently to protein-related tasks. Here, information from each representation (protein sequence, 3D structure, and interaction data) is combined for an efficient protein function prediction task. Recently, uni-modal has produced promising results with state-of-the-art attention mechanisms that learn the relative importance of features, whereas multi-modal approaches have produced promising results by simply concatenating obtained features using a computational approach from different representations which leads to an increase in the overall trainable parameters. In this paper, we propose a novel, light-weight cross-modal multi-attention (CrMoMulAtt) mechanism that captures the relative contribution of each modality with a lower number of trainable parameters. The proposed mechanism shows a higher contribution from PPI and a lower contribution from structure data. The results obtained from the proposed CrossPredGO mechanism demonstrate an increment in Fmax in the range of +(3.29 to 7.20)% with at most 31% lower trainable parameters compared with DeepGO and MultiPredGO.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
ACM Trans Comput Biol Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article