Abstract
task：Protein-Protein Interaction task
introduced a novel framework that enables the model to learn multi-omnics
biological information about entities (proteins)
with the help of additional multi-modal cues
like molecular structure.
devise a generalized and optimized graph based multi-modal learning mechanism that
utilizes the GraphBERT model
The main contributions：
utilized protein
atomic structural information while identifying the protein interactions
developed a generalized modality-agnostic
approach
2 Proposed Method
four main components:
(1)
Multi-modal Graph Constructor
,
(2)
Multi
-modal Graph Fusion
(3)
Multi-modal Graph En
coder
(4)
PPI Predictor
.
Problem Statement:
a biomedical input text：
S
=
{
w
1
, w
2
, . . . , w
n
}
a pair of protein mentions：
p
1
, p
2
∈
S
predict:
‘interact’
or
‘non-interact’
2.1 Multi-modal Graph Constructor
Textual Graph Constructor：
constructs
the graph by considering the textual content that
aims to capture the lexical and contextual informa
tion present in the input
Protein Structure Graph Constructor：
exploits the atomic structure (3D PDB structure) of the protein
molecules to build the graph.
2.2 Multi-modal Graph Fusion
2.3 Multi-modal Graph Encoder
2.4 PPI Predictor
3 Datasets and Experimental Analysis
4 Error Analysis