LSAGSL focuses on studying the correlation between different extracted features and proposes a GNN algorithm based on a multi-layer self-attention mechanism. In the process of gene information aggregation, LSAGSL requires that the information source exhibits a strong association with both target genes.Furthermore, LSAGSL enhances the detection of hierarchical correlations within individual genes and the overall correlation between pairs of genes through the incorporation of a dual-layer attention mechanism within genes and a singular fused attention mechanism between gene pairs. This enables the extraction of crucial information pertaining to the correlation between gene pairs for the purpose of SL prediction.
- SL data: 32,561 SL gene pairs involving 9,516 genes are used as our SL labels.
- SynLethKG: knowledge graph consists of 54,012 nodes of 11 types and 2,231,921 edges of 24 types of relations.
We use 5-fold cross-validation (CV) in the following three evaluation settings
| Evaluation schems | Description |
|---|---|
| C1 | Dataset is split by gene pairs, where both genes of a pair in test set can be present in the train set. |
| C2 | Dataset is split by genes, where exactly one gene of a pair in test set is present in the train set. |
| C3 | Dataset is split by genes, where both genes of a pair in test set are not used in train set. |
pip install -r requirements.txtpython train.py
-d C1/cv_1 # evalution schem
-e C1/cv_1 # the name for the log for experiments
--gpu=0 # ID of GPU
--hop=3 # size of the hops for enclosing grpah
--batch=512 # batch size for samples
--emb_dim=64 # size of embedding for GNN layers
-b=10 # size of basis for relation kernel
--l2=0.0001 # coefficient of regularizer
-l=3 # number of GCN layers