ArticleBioinformatics (Oxford, England)2025
HECLIP: histology-enhanced contrastive learning for imputation of transcriptomics profiles.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- DeepPathway: predicting pathway expression from histopathology images.Bioinformatics (Oxford, England) · 2026Article
- Tunable acoustic rotation for deep biophysical phenotyping of preclinical Alzheimer's disease.Materials today. Bio · 2026Article
- Predicting protein cascade expression from H&E images.PLoS computational biology · 2026Article
- Multimodal magnetic resonance imaging (MRI) quantification of hippocampal injury in acute carbon monoxide poisoning: reduced hippocampal volume as a sensitive biomarker linked to cognitive impairment and prognosis.Quantitative imaging in medicine and surgery · 2026Article
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities.Frontiers in public health · 2026Review
- HiGATE: hierarchical graph attention for multi-scale tissue encoder in computational pathology.Frontiers in oncology · 2026Article
- ICMC: An Interpretable Cross-domain Multi-modal Classification model for grading teaching plan.PloS one · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
motivationHistopathology, particularly hematoxylin and eosin (H&E) staining, is pivotal for diagnosing and characterizing pathological conditions by visualizing tissue morphology. However, H&E-stained images inherently lack molecular resolution, necessitating costly and labor-intensive technologies like spatial transcriptomics (ST) to uncover spatial gene expression patterns. There is a critical need for scalable computational methods that can bridge this imaging-transcriptomics gap.
resultsWe present histology-enhanced contrastive learning for imputation of profiles (HECLIP), an innovative deep learning framework designed to infer spatial gene expression profiles directly from H&E-stained histology images. HECLIP employs an image-centric contrastive learning strategy to capture morphological features relevant to molecular expression. By minimizing dependence on ST data, HECLIP enables accurate and biologically meaningful predictions of gene expression. Extensive benchmarking on publicly available datasets demonstrates that HECLIP outperforms existing methods. Ablation studies confirm the contribution of each model component to its overall performance. AVAILABILITY AND IMPLEMENTATION: The source code for HECLIP is freely available at: https://github.com/QSong-github/HECLIP.
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