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. 2023 Dec 1;25(1):bbad379. doi: 10.1093/bib/bbad379

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© The Author(s) 2023. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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(A) The structure of the HiC-SGL model. It consists of an encoder and a decoder. The encoder extracts the local feature of each edge and the global feature of the cell graph. The decoder estimates the likelihood of each edge being present in the cell map using the encoded feature. (B) Subgraph Encoder, implemented by variant of Transformer on graph neural networks. : Node Embeddings of layer ; E: adjacency matrix of subgraph.

Inline graphic — (A) The structure of the HiC-SGL model. It consists of an encoder and a decoder. The encoder extracts the local feature of each edge and the global feature of the cell graph. The decoder estimates the likelihood of each edge being present in the cell map using the encoded feature. (B) Subgraph Encoder, implemented by variant of Transformer on graph neural networks. : Node Embeddings of layer ; E: adjacency matrix of subgraph.