RT Journal Article
SR Electronic
T1 Pathformer: biological pathway informed Transformer model integrating multi-modal data of cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.05.23.541554
DO 10.1101/2023.05.23.541554
A1 Xiaofan Liu
A1 Yuhuan Tao
A1 Zilin Cai
A1 Pengfei Bao
A1 Hongli Ma
A1 Kexing Li
A1 Yunping Zhu
A1 Zhi John Lu
YR 2023
UL http://biorxiv.org/content/early/2023/05/24/2023.05.23.541554.abstract
AB Multi-modal biological data integration can provide comprehensive views of gene regulation and cell development. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability. To address these challenges, we developed Pathformer, a biological pathway informed deep learning model based on Transformer with bias to integrate multi-modal data. Pathformer leverages criss-cross attention mechanism to capture crosstalk between different biological pathways and between different modalities (i.e., multi-omics). It also utilizes SHapley Additive Explanation method to reveal key pathways, genes, and regulatory mechanisms. Through benchmark studies on 28 TCGA datasets, we demonstrated the superior performance and interpretability of Pathformer on various cancer classification tasks, compared to other integration models. Furthermore, we applied Pathformer to liquid biopsy multi-modal data integration with high accuracy in cancer diagnosis. Meanwhile, Pathformer revealed interesting molecularly altered pathways in cancer patients’ body fluid, such as ligand binding of scavenger receptors, iron transport, and DAP12 signaling transmission, which are related to extracellular vesicle transport, platelet, and immune response.Competing Interest StatementThe authors have declared no competing interest.