🧬 Computational Biology

📷 CVPR2026 · 5 paper notes

📌 Same area in other venues: 🧪 ICML2026 (8) · 💬 ACL2026 (2) · 🔬 ICLR2026 (24) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (44) · 📹 ICCV2025 (3)

🔥 Top topics: Biomolecules ×3

CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis

This paper proposes CARE, a slide-level pathology foundation model that employs an Adaptive Region Generator (ARG) to partition WSIs into morphologically coherent irregular regions (analogous to word-level tokens in NLP), combined with two-stage pretraining via cross-modal alignment with RNA/protein expression profiles. Using approximately 1/10 the data of mainstream models, CARE achieves state-of-the-art average performance across 33 downstream tasks.

Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images

This paper proposes CPNN, which constructs cell-type prototypes from publicly available single-cell RNA-seq data and models slide/patch-level gene expression as a weighted combination of these prototypes, achieving state-of-the-art performance on gene expression estimation while providing interpretability.

Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning for Spatial Gene Expression Inference

This paper proposes SpaHGC, a multimodal heterogeneous graph framework that constructs three types of subgraphs—intra-target-slice (TS), cross-slice (CS), and intra-reference-slice (RS)—and integrates masked graph contrastive learning with a cross-node dual attention mechanism to predict spatial gene expression from H&E histopathology images, achieving PCC improvements of 7.3%–27.1% across seven datasets.

cryoSENSE: Compressive Sensing Enables High-throughput Microscopy with Sparse and Generative Priors on the Protein Cryo-EM Image Manifold

This paper proposes cryoSENSE, the first computational framework for compressed cryo-EM imaging, demonstrating that protein cryo-EM images can be faithfully reconstructed from undersampled measurements under both sparse priors (DCT/Wavelet/TV) and generative priors (diffusion models), achieving up to 2.5× throughput gain while preserving 3D reconstruction resolution.

Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation

This paper proposes ERBA (Enzyme-Reaction Bridging Adapter), which reformulates enzyme kinetic parameter prediction as a staged multimodal conditional generation problem — first injecting substrate information via MRCA to capture substrate recognition specificity, then integrating active-site 3D geometry via G-MoE to capture conformational adaptation, with ESDA distribution alignment to preserve PLM semantic priors.