MOTIVATION: The assessment of aberrant transcription events in rare disease patients holds great promise for enhancing the prioritization of causative genes-a strategy already widely adopted in clinical settings to improve diagnostic accuracy. Nevertheless, the accurate identification of causal genes remains a substantial challenge. RESULTS: We propose AXOLOTL, a novel ensemble method for identifying aberrant gene expression events in RNA expression matrices. AXOLOTL effectively accounts for gene correlation by incorporating coexpression constraints. We demonstrated the superior performance of AXOLOTL on representative RNA-seq datasets, including those from the GTEx healthy cohort, mitochondrial disease cohorts, and collagen VI-related dystrophy cohorts. Furthermore, we applied AXOLOTL to real-world cases of neurological disorders and demonstrated its ability to accurately identify aberrant gene expression and facilitate the prioritization of pathogenic variants. AVAILABILITY AND IMPLEMENTATION: AXOLOTL is freely available on GitHub (https://github.com/xuwenjian85/axolotl) and Zenodo (https://doi.org/10.5281/zenodo.17940844).

Vulnerability attacks targeting smart contracts have caused significant losses of digital assets. Many approaches based on static analysis, fuzzing, and deep learning have been proposed for detecting contract vulnerabilities. However, most existing methods only support vulnerability detection within individual contracts. When contracts interact with each other through external calls, these methods fail to perform effective cross-contract security analysis, leading to false negatives and false positives. To address these limitations, we propose CrossMiner, a deep learning-based approach for vulnerability detection in contract interaction scenarios. CrossMiner enables comprehensive risk assessment for cross-contract security through trace analysis of function call chains. Specifically, CrossMiner first constructs a cross-contract dependency graph based on function call chains to effectively model inter-contract dependencies and network dynamics, and collect semantic information about contract interactions. Then, it employs a heterogeneous graph neural network with a two-level attention mechanism to finely extract and integrate complex features from the dependency graph, ultimately achieving precise risk assessment and vulnerability detection. We evaluate the effectiveness of CrossMiner on three types of smart contract vulnerabilities: reentrancy, timestamp dependency, and transaction state dependency. Experimental results demonstrate that CrossMiner achieves the best performance among all baseline methods, improving detection accuracy by 5.52%, 4.94%, and 5.60% for these vulnerabilities, and the F1 scores are improved by 5.44%, 5.02%, and 5.40%, respectively.

Smart contract vulnerabilities have led to significant economic losses, threatening blockchain security and development. Graph neural network (GNN)-based approaches, which capture the structural properties of contracts and leverage code dependencies to better understand contract behavior, have become widely used for vulnerability detection. However, these approaches face challenges in losing valuable information during graph construction and failing to capture rich semantic content, while traditional GNNs struggle with long-range dependencies and global context in complex contract graphs. To address these challenges, we propose ConSense, a GNN-based Contract Sensing Framework for Smart Contract Vulnerability Detection. ConSense comprises two core components: the smart contract graph generator, which constructs contract graphs while retaining both structural and semantic information, and ExploreFormer, which effectively integrates local and global context using advanced attention mechanisms for vulnerability detection. Comprehensive experimental evaluations were performed on the IR-ESCD and SCVHunter-SCD datasets. For instance, the IR-ESCD benchmark—which encompasses eight distinct vulnerability categories—demonstrates that ConSense attains an average detection accuracy of 97.74%, with a mean processing time of 0.648 seconds per contract. These results signify a statistically significant improvement over state-of-the-art methods in both precision and computational efficiency.