3,462 research outputs found
Interpreting mechanism of Synergism of drug combinations using attention based hierarchical graph pooling
The synergistic drug combinations provide huge potentials to enhance
therapeutic efficacy and to reduce adverse reactions. However, effective and
synergistic drug combination prediction remains an open question because of the
unknown causal disease signaling pathways. Though various deep learning (AI)
models have been proposed to quantitatively predict the synergism of drug
combinations. The major limitation of existing deep learning methods is that
they are inherently not interpretable, which makes the conclusion of AI models
un-transparent to human experts, henceforth limiting the robustness of the
model conclusion and the implementation ability of these models in the
real-world human-AI healthcare. In this paper, we develop an interpretable
graph neural network (GNN) that reveals the underlying essential therapeutic
targets and mechanism of the synergy (MoS) by mining the sub-molecular network
of great importance. The key point of the interpretable GNN prediction model is
a novel graph pooling layer, Self-Attention based Node and Edge pool
(henceforth SANEpool), that can compute the attention score (importance) of
nodes and edges based on the node features and the graph topology. As such, the
proposed GNN model provides a systematic way to predict and interpret the drug
combination synergism based on the detected crucial sub-molecular network. We
evaluate SANEpool on molecular networks formulated by genes from 46 core cancer
signaling pathways and drug combinations from NCI ALMANAC drug combination
screening data. The experimental results indicate that 1) SANEpool can achieve
the current state-of-art performance among other popular graph neural networks;
and 2) the sub-molecular network detected by SANEpool are self-explainable and
salient for identifying synergistic drug combinations
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
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