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Comprehensive evaluations across 15 biological datasets demonstrate that scGADSSC outperforms ten state-of-the-art single-cell clustering methods on most test datasets, achieving superior clustering performance.
DMGRN, a novel GRN inference frame work built upon an improved Denoising Diffusion Probabilistic Model (DDPM), achieves the highest Early Precision Ratio (EPR) on 18 out of 28 BEELINE benchmark configurations, demonstrating superior stability and computational efficiency compared to state-of-the-art methods.
Comprehensive evaluations on multiple real-world networks demonstrate KSvoterank’s superior performance in propagation scope and robustness, validating its utility for influence maximization and network robustness analysis.
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