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Liang Xue

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3 papers

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AAAI Conference 2025 Conference Paper

Association Pattern-enhanced Molecular Representation Learning

Lingxiang Jia
Yuchen Ying
Tian Qiu
Shaolun Yao
Liang Xue
Jie Lei
Jie Song
Mingli Song

The applicability of drug molecules in various clinical scenarios is significantly influenced by a diverse range of molecular properties. By leveraging self-supervised conditions such as atom attributes and interatomic bonds, existing advanced molecular foundation models can generate expressive representations of these molecules. However, such models often overlook the fixed association patterns within molecules that influence physiological or chemical properties. In this paper, we introduce a novel association pattern-aware message passing method, which can serve as an effective yet general plug-and-play plugin, thereby enhancing the atom representations generated by molecular foundation models without requiring additional pretraining. Additionally, molecular property-specific pattern libraries are constructed to collect the generated interpretable common patterns that bind to these properties. Extensive experiments conducted on 11 benchmark molecular property prediction tasks across 8 advanced molecular foundation models demonstrate significant superiority of the proposed method, with performance improvements of up to approximately 20%. Furthermore, a property-specific pattern library is tailored for blood-brain barrier penetration, which has undergone corresponding mechanistic validation.

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EAAI Journal 2025 Journal Article

Hyperspectral imaging for rapid impurity detection in power system liquids

Liang Xue
Li Zhang
Zhuoyue Yang
Youhua Jiang
Chao Jiang
Haoyang Cui

The chemical integrity of power system liquids, such as coolants and transformer oils, is critical for the reliable operation of energy systems. Contaminants such as carbon, iron, copper, and tin can compromise cooling efficiency, increase failure risks, reduce equipment lifespan, and cause electrical malfunctions, thereby threatening the safety and stability of these systems. This study presents an innovative approach that integrates hyperspectral imaging (HSI) with machine learning (ML) algorithms to identify and quantify impurities in these liquids. A weighted ensemble model, referred to as the WeightedEnsemble_L2 model, has been developed and optimized. This model utilizes thirteen advanced machine-learning algorithms to identify impurities by analyzing spectral signatures across a broad wavelength range. The implemented artificial intelligence (AI) model demonstrates 90 % accuracy on the training set and 87. 53 % on the validation set. This novel approach offers a robust solution for impurity detection in power system liquids, supporting predictive maintenance and enhancing the safety and stability of energy systems through the practical application of AI technology.

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EAAI Journal 2024 Journal Article

Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data

Liang Xue
Tianqing Zhu

In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is biased towards the majority class. Insufficient data makes it challenging for the classifier to learn the feature of the minority classes. Moreover, some existing techniques of binary classification cannot directly apply to multi-classification. Plain oversampling may generate redundant and irrelevant data for the minority classes while undersampling may eliminate too many features for the majority class. This paper proposes a combination of constrained undersampling, oversampling, noise cleaning, and a weighted majority voting classifier (WMVC) to detect multi-classification obfuscated malware via memory and network traffic anomalies. As a constraint on undersampling and oversampling, the proposed framework divides the total sample number by the class number to obtain the average class size. According to the constraint, one or more majority classes are down-sampled using Random Undersampling, while all the minority classes are up-sampled using Adaptive Synthetic Sampling Approach, which is followed by Tomek Link to remove the noisy data. Then a weighted majority voting classifier aggregated tree-based ensemble algorithms is designed and compared to the XGBoost and Convolutional Neural Network (CNN) classifiers and six state-of-the-art ensemble algorithms. The comparison results show that the performance of the classifiers on balanced data outperforms those on imbalanced data, and the WMVC outperforms XGBoost, CNN, and the six other ensemble algorithms. Our approach can alleviate the classifier's bias towards the majority class while improving its performance for the difficult minority class.

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