Abstract Motivation Accurately predicting anticancer drug response is a central challenge in precision oncology. Existing computational methods, although valuable, often depend on pairwise molecular descriptors or limited graph-based encodings that cannot fully capture the complexity of molecular structures or their in...
Cong Shen, Guan-Cen Lin, Chuan-Shen Hu et al.· Bioinform.· 0 citations
LaCONIC is proposed, a label-aware and graph-guided multi-omics collaborative learning framework that bridges fine-grained molecular regulation and coarse-grained patient prognosis and consistently outperforms 14 representative survival baselines.
Pei Liu, Xiao Liang, Jia-Wei Luo· Proceedings of the 32nd ACM...· 0 citations
Proteolysis-targeting chimeras (PROTACs) have emerged as a transformative therapeutic strategy that selectively degrades historically''undruggable''targets via the ubiquitin-proteasome system. Despite growing efforts to develop computational predictors of PROTAC degradation activity, existing supervised approaches rema...
Yuansheng Liu, Yu-Fei Ye, Tao Tang et al.· 0 citations
Accurate cancer survival prediction is important for risk stratification and personalized treatment. However, patient prognosis is shaped by complex molecular regulation, cross-omics dependencies, and subtype-dependent tumor heterogeneity, making accurate and interpretable prediction challenging. Existing multi-omics s...
Pei Liu, Xiao Liang, Jiawei Luo· Proceedings of the 32nd ACM...· 0 citations
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