Biological Pathway Informed Models with Graph Attention Networks (GATs)
Gavin Wong, Ping Shu Ho, Ivan Au Yeung, Ka Chun Cheung, Simon See
生物途径绘制了支配所有人类过程的基因-基因相互作用。 尽管它们很重要,但大多数ML模型将基因视为非结构化的令牌,丢弃了已知的通路结构。 最新的通路知情模型捕获了通路-途径相互作用,但仍通过MLP将每个通路视为“基因袋”,丢弃其拓扑和基因基因相互作用。 我们提出了一个图形注意力网络(GAT)框架,该框架可以模拟基因水平的路径。 我们表明,GAT的普及比MLP好得多,实现了81
Biological pathways map gene-gene interactions that govern all human processes. Despite their importance, most ML models treat genes as unstructured tokens, discarding known pathway structure. The latest pathway-informed models capture pathway-pathway interactions, but still treat each pathway as a "bag of genes" via MLPs, discarding its topology and gene-gene interactions. We propose a Graph Attention Network (GAT) framework that models pathways at the gene level. We show that GATs generalize m...