As the volume of omics data continues to grow exponentially, there is an increasing demand for innovative methodologies that combine multi-omics data to extract meaningful clinical insights. Absolute cell counts are a fundamental component of clinical evaluations for disease diagnosis, treatment, and patient management. While cellular deconvolution can estimate relative cell type proportions from bulk data, obtaining absolute cell counts from omics data remains rarely studied. In response to the clinical needs and challenges, we introduce a novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC). This model is designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data. Comprehensive evaluations, including cross-validation, independent data testing, and real-world applications, demonstrate the model's robustness, precision, and capacity to effectively capture biological variations. MOFUN-CCC represents a pioneering effort in the integration of multi-omics data for the prediction of absolute cell counts. With our user-friendly software (https://github.com/yuemolin/MOFUN-CCC) and web application (https://shiny.crc.pitt.edu/mofun_shiny/), this innovation holds the potential to make significant contributions to disease diagnosis, progression analysis, and clinical decision-making.
Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (MLaaS). Still, their black-box deployment exposes them to Model Extraction (ME) attacks, in which adversaries steal intellectual property by querying APIs. Existing defenses suffer from a critical ''Euclidean bias'': they transfer image-based strategies (e.g., random noise) to graphs, ignoring the complex topological dependencies between nodes, which often results in severe utility degradation. Passive methods like watermarking also fail to prevent theft in real time. To bridge this gap, we propose GraphRP (Graph Reprogramming Protection), a proactive defense framework that repurposes Model Reprogramming for security. Unlike static perturbations, GraphRP introduces a Structure-Aware Gating Mechanism driven by learnable topological prototypes. This creates a dynamic ''structural firewall'' that selectively modulates the model's decision boundary: it preserves fidelity for benign queries residing on the training manifold, while maximizing the Fisher Information along the perturbation direction for adversarial queries. Under standard assumptions (bounded loss, optimal attacker, and local second-order approximation), we prove a lower bound on the attacker's estimation error that increases with the structural sensitivity of the reprogramming noise. Extensive experiments on both hard-label and soft-label ME attacks demonstrate that GraphRP significantly degrades attack effectiveness while preserving benign utility.
Yan Wen, Zhenyi Wang, Heng Huang· Proceedings of the 32nd ACM...· 0 citations