A contrastive pretraining framework that learns cell representations through complementary transcriptomic views by constructing two complementary views of each cell and introducing a competence-aware controller to determine how the contrastive objective is applied is established.
Jia-Qi Xiong, Yun-Tao Hu, Yu Zheng et al.· 0 citations
PopPert predicts perturbation-induced changes in distribution parameters, eliminating the need for cell-level correspondence and reducing sensitivity to single-cell noise, establishing population-level joint distribution learning as an effective paradigm for predicting transcriptional responses from unpaired single-cel...
Han-Dong Wang, Jiaxin Qi, Hao-Chen Feng et al.· 0 citations
This work introduces a symmetric dual-path architecture that both leverages PLMs for pretrained sequence evolution knowledge and MPLMs for pretrained structural knowledge to iteratively guide protein sequence generation.
Han-Dong Wang, Jiaxin Qi, Baisheng Lai et al.· 0 citations
Self-Generative-Understanding (SGU), a novel, annotation-free evaluation framework that probes the integrated capabilities of unified models through a semantic closed-loop challenge, provides a complementary holistic evaluation framework and offers a foundation for benchmarking the development of next-generation unifie...
Hao Zhang, Jiaxin Qi, Zhijiang Tang et al.· 0 citations
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