Zheng Zhao

Motivation: N6-methyladenine (6 mA) is an important epigenetic modification of DNA that regulates biological processes such as gene expression, transcription, replication, DNA repair, and cell cycle without altering the DNA sequence. It also plays a key role in many diseases including cancer and autoimmune diseases. Although experimental approaches such as SMRT sequencing and methylated DNA immunoprecipitation can identify 6 mA sites, they suffer from drawbacks including suboptimal sequencing quality, low signal-to-noise ratios, high costs, and time-consuming procedures. In recent years, deep learning approaches have demonstrated significant advantages in predicting 6 mA sites; however, their generalization ability still requires further improvement. Results: Inspired by the state space model Mamba, we propose a novel model for 6 mA site prediction, named Mamba6mA. In the Mamba6mA model, we design position-specific linear layers to replace traditional convolutional layers to facilitate capture specific positional information. Meanwhile, we construct a multi-scale feature extraction module and integrate features captured by sliding windows of different scales, feeding them into the classifier for prediction. Experimental results show that Mamba6mA achieves the best MCC on 9 out of 11 species datasets, surpassing existing state-of-the-art models. Ablation studies confirm that the position-specific linear layers and the multi-scale fusion module contribute MCC performance gains of 2.36% and 2.31%, respectively. Feature visualization analysis further reveals that the model effectively captures sequence patterns upstream and downstream of 6 mA sites providing a new technical approach for studying epigenetic modification mechanisms. Availability and implementation: The source code for Mamba6mA is available at: https://github.com/XploreAI-Lab/Mamba6mA.

Source code vulnerability detection via graph learning is one of the most important approaches to maintain software security, as it enables structural analysis of semantic dependencies within programs. However, it may suffer from vulnerability coverage, semantic sparsity, trigger path identification, especially when those vulnerabilities do not involve API/library calls. In this article, we present FORTIFY, a graph learning framework that couples feature representation tightly with program topology to perform path-level vulnerability detection. Beginning with a program dependence graph, FORTIFY reconstructs its Sliced Combined Graph (SCG) using program slicing with diverse edges. The SCG is then generated as a weighted edge hypergraph, enabling the model to capture both local semantic and structure relationships. Through path embeddings, we introduce an adaptive hyperedge-aware strategy to allocate high capacity vectors reaching security sensitive nodes. A relation-aware graph convolutional network, equipped with risk sensitive attention and an Information Noise Contrastive Estimation (InfoNCE) objective, further amplifying the weights of high risk paths. Experimental results on the publicly available datasets (i.e., SARD, NVD, and FFmpeg-Vul) show that FORTIFY can identify the execution paths of vulnerabilities. We also test it on real world software such as the PX4 open-source drone, and it finds that there are control type vulnerabilities in PX4, verifying that FORTIFY can be used for the analysis of programs including unmanned agents. The implementation of FORTIFY is publicly available at https://github.com/ACoTAI/FORTIFY .

DNA methylation is a key epigenetic modification involved in biological processes and disease development. The accurate analysis of DNA methylation site information is of significant biological importance. Despite advances in single-cell sequencing, data sparsity due to low cytosine-phosphate-guanine (CpG) coverage remains a challenge. To address this, we introduce MambaCpG, a single-cell DNA methylation state imputation model based on the Mamba block. MambaCpG integrates the methylation matrix and DNA sequence context and uses bidirectional Mamba blocks to obtain embeddings, effectively capturing the long-range dependencies between CpG sites within DNA methylation patterns. Experiments on seven datasets of various scales demonstrate that MambaCpG outperforms existing models on large, highly sparse datasets while showing competitive performance on smaller datasets. MambaCpG has lower parameter and memory requirements, making it suitable for practical applications. MambaCpG also reveals ultra-long-range dependencies and provides new insights into DNA methylation patterning.

The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios.

Sniffing attack is one of the typical passive network attack methods, which poses a serious threat to network communication security. Detecting and defending against sniffing attacks is challenging due to their stealthy nature. In this paper, we propose an encryption hopping method based on a programmable data plane named AP-EH to defend against sniffing attacks. By leveraging the concept of Moving Target Defense (MTD), AP-EH greatly elevates the complexity and cost for attackers to crack the communication data by dynamically modifying the encryption algorithm and key. Experimental results and analysis demonstrate the security and performance of the proposed method.

Recommendation systems have become an indispensable part of daily life. Social recommendation systems, which utilize social relationships and past behaviors to infer users’ preferences, have gained popularity in recent years. Exploring the inherent characteristics implied by higher-order relationships offers a new approach to social recommendation. However, it is challenging due to sparse social networks, influence heterogeneity, and noisy feedback. In this article, we propose a Heterogeneous Meta-path Graph Learning model for Higher-order Social Recommendation (HEAL). Within HEAL, we introduce a heterogeneous graph in social recommendation and utilize a meta-path-guided random walk to generate higher-order relationships. By encoding higher-order structures and semantics along different meta-graphs, HEAL can mitigate the limitation of data sparsity. Moreover, HEAL exploits aspect-aware and semantic-aware attentions to adaptively propagate and aggregate useful features from different meta-neighbors and higher-order relations. These attention-based aggregation layers allow HEAL to suppress the heterogeneity of social influences. Furthermore, HEAL adopts contrastive learning as a supplemental task to the recommendation task by maximizing the consistency between the self-discriminating objectives. This auxiliary task enables the model to learn more differentiated representations, further reducing its sensitivity to noisy feedback. We evaluate the performance of HEAL through extensive experiments on public datasets. The results demonstrate that leveraging higher-order relations can enhance the quality of social recommendations by better capturing the complexity and diversity of users’ preferences and interactions.