Predicting single-cell gene responses to chemical perturbations is vital for personalized therapy, yet existing deep learning models face significant hurdles. Standard regression-based approaches suffer from "mean prediction bias," failing to capture cellular heterogeneity, while current Flow Matching (FM) methods stru...
H. Lyu, Jia-Wei Luo· Proceedings of the Thirty-Fi...· 0 citations
Abstract Motivation Conventional drug perturbation prediction models typically employ end-to-end encoder-decoder architectures, directly mapping control samples and perturbation conditions to post-perturbation gene expression profiles. However, these approaches widely overlook the severe class imbalance inherent in per...
MolDr is a topological deep learning framework that represents molecules as multiscale simplicial complexes and propagates information across higher-order structures and unifies chemical topology and biological context within a single predictive model.
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
ProMeta, a prototype-based graph neural network trained through episodic meta-learning on source-E3 tasks and evaluated on held-out target-E3 tasks through support-conditioned inference, is presented as a practical framework for cross-ligase few-shot prediction under the evaluated support/query protocols.
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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