  Synthetic lethality (SL) prediction is used to identify if the co-mutation of
two genes results in cell death. The prevalent strategy is to abstract SL
prediction as an edge classification task on gene nodes within SL data and
achieve it through graph neural networks (GNNs). However, GNNs suffer from
limitations in their message passing mechanisms, including over-smoothing and
over-squashing issues. Moreover, harnessing the information of non-SL gene
relationships within large-scale multi-omics data to facilitate SL prediction
poses a non-trivial challenge. To tackle these issues, we propose a new
multi-omics sampling-based graph transformer for SL prediction (MSGT-SL).
Concretely, we introduce a shallow multi-view GNN to acquire local structural
patterns from both SL and multi-omics data. Further, we input gene features
that encode multi-view information into the standard self-attention to capture
long-range dependencies. Notably, starting with batch genes from SL data, we
adopt parallel random walk sampling across multiple omics gene graphs
encompassing them. Such sampling effectively and modestly incorporates genes
from omics in a structure-aware manner before using self-attention. We showcase
the effectiveness of MSGT-SL on real-world SL tasks, demonstrating the
empirical benefits gained from the graph transformer and multi-omics data.
