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.+Graph+Convolutional+Network+(GCNs)%252C+Chebyshev+spectral+graph+convolution+(ChebyNet)%252C+and+Graph+Attention+Network+(GAT)%252C+are+evaluated+alongside+advanced+architectures+like+graph+transformers%252C+the+graph+U-net%252C+and+simpler+models+like+multilayer+perceptron+(MLP).%250A++These+models+are+systematically+applied+to+transcriptomics+and+metabolomics+data+independently.+Our+comparative+analysis+highlights+the+benefits+and+limitations+of+various+architectures+in+extracting+patterns+from+omics+data%252C+paving+the+way+for+more+accurate+and+interpretable+models+in+biomedical+research.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.ebe85edba6e3988de0e3ba02d8b00725%3frik%3d8rJ0%252fy%252fkQIDKnQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Omics+Graph&FORM=IRPRST&ck=4DE2B8C96F84125E1A3546BB624A7608&selectedIndex=4&itb=0)
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