Understanding the molecular mechanisms of complex diseases requires insight into cellular interactions and protein expression. While large-scale sequencing enables disease subtyping and patient stratification, integrating proteomics and transcriptomics data offers a deeper view of cellular states. Recent methods combine scRNA-seq, which provides broad cellular coverage, with transcriptomics and proteomics co-profiling, which provides more comprehensive molecular measurements. However, many models adopt simplistic strategies for joint analysis. We introduce scProca, a deep generative model that incorporates inter-cellular relationships via cross-attention mechanisms to handle heterogeneous inputs, whether from RNA-seq or co-profiling datasets. scProca achieves state-of-the-art integration and imputation, remains robust under high protein sparsity, generalizes across species and tissues, scales to large datasets, and is compatible with experimental batches, demonstrating strong flexibility for complex experimental settings.

Clustered regularly interspaced short palindromic repeats (CRISPR) gene-editing technology has transformed molecular biology. Predicting editing efficiency is crucial for optimization, and numerous computational models have been created. However, many current models struggle to generalize across diverse editing systems, often experiencing performance drops with varying conditions or systems. Additionally, most models focus on ribonucleic acid (RNA) sequence and thermodynamic features, overlooking the importance of secondary structure information. Here, we present the first graph-based model (Graph-CRISPR) that integrates both sequence and secondary structure features of single guide RNA enhancing editing efficiency prediction. Tests show Graph-CRISPR consistently surpasses baseline models across systems like CRISPR-Cas9, prime editing, and base editing. It also demonstrates strong resilience, maintaining robust performance under varying experimental conditions. This work highlights the potential of integrating sequence and structural information through graph-based modeling to enhance predictive accuracy and adaptability in gene editing applications. The datasets and source codes are publicly available at: https://github.com/MoonLBH/Graph-CRISPR.