Single-cell transcriptomics resolves CAR T-cell states, yet translating heterogeneous cellular signals into patient-level therapeutic response remains challenging. Existing studies primarily identify response-associated genes or cell populations through experimental and statistical analyses, but few predictive frameworks integrate gene-level structure with clinical outcomes. Here, we present gANCHOR, a T-cell foundation model built on a hierarchical hypergraph attention framework combining biologically informed representation learning with patient-level response prediction. By encoding gene-pathway relationships, gANCHOR learns pathway-aware cell embeddings that improve biological conservation and batch robustness. A cell-to-patient attention module then aggregates cellular information to infer therapeutic response. Across benchmark datasets, gANCHOR achieved the strongest overall performance in biological conservation and batch-correction assessments. In response prediction across 161 patients from five CAR T-cell studies, gANCHOR achieved an F1 score of 0.87, outperforming benchmarked single-cell foundation models. gANCHOR also identified reproducible response- and non-response-associated gene programs, providing interpretable biological insights into CAR T-cell efficacy and resistance.
Yawei Li, Deyu Fang, Chengsheng Mao et al.· bioRxiv· 0 citations
Contrastive vision–language pretraining models such as CLIP align images and text in a shared embedding space but do not explicitly model or evaluate the hierarchical semantics common in medical image interpretation. We propose HCE-CLIP (Hierarchical Conditional Energy CLIP), a vision–language pretraining framework that formulates medical image–text alignment as a hierarchical label-conditional energy modeling problem. HCE-CLIP encodes an image series using transformer-based aggregation and aligns it with free-text reports and structured label state prompts across multiple semantic levels. At each level, conditional energy functions favor clinically consistent label states while suppressing contradictory alternatives, enabling uncertainty-aware inference. To assess semantic coherence, we introduce a hierarchical contradiction-based metric that quantifies logical inconsistencies between fine-grained disease predictions and higher-level clinical summaries. Experiments on MIMIC-CXR and other public benchmarks show that HCE-CLIP outperforms existing medical vision–language pretraining methods in seen-label, zero-shot and linear-probe settings, while producing substantially fewer hierarchical contradictions.
Cheng-Sheng Mao, Yuan Luo· Proceedings of the Thirty-Fi...· 0 citations
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