pcbi.1011935.s001.docx (26.97 kB)
Supporting information.
journal contribution
posted on 2024-02-28, 18:37 authored by Chen Zhang, Junhui Gao, Hong-Yu Chen, Lingxin Kong, Guangshuo Cao, Xiangyu Guo, Wei Liu, Bin Ren, Dong-Qing WeiS1 Text: Rationale of Graph Convolution Neural Network (GCN) and the Adaptive Graph Convolution (AGC) in STGIC.
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transcriptionally similar togetherkullback – leiblerhigh confidence pseudogat ), feddepicting fine structuresdilated convolution frameworkvirtual image convertedgraph attention networkgene expression informationspatial continuity lossgraph convolution networkadaptive graph convolutionstgic attains statediv >< pg c clustering employs spatialimage convolutiontranscription informationspatial transcriptomicspatial domainspatial distancespatial coordinatesranscriptomic clusteringclustering performanceweight valuestraining objectivesupervision realizedset appropriatelyseq dataregular latticesoften usedmarker geneskernel sizeskernel centersfeature extractiondlpfc ).dilation ratescorresponding elementsbetter guidedbenchmark datasetbased method
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