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Jing Feng

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4 papers

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AAAI Conference 2026 Conference Paper

Domain-Aware Multi-View Contrastive Representation Learning for Protein Subcellular Localization Prediction

Qiang Zhang
Feng Yang
Weihong Huang
Jing Feng
Juan Liu

Protein subcellular localization prediction is essential for understanding protein function and cellular organization. However, existing methods exhibit two major limitations: (1) they overlook the critical role of evolutionarily conserved protein domains, which are fundamental functional and structural units that significantly influence functions and subcellular localization, and (2) they rarely learn residue order and backbone coordinates simultaneously, neglecting the complementary information inherent in multi-modal representations. In this paper, we propose a novel Domain-Aware Multi-View Contrastive Representation Learning for Protein Subcellular Localization prediction, named DMVCL. Firstly, it devises domain-sequence/structure attention modules, which identify functionally significant regions in protein structures/sequences that critically determine subcellular localization. Secondly, it introduces a multi-view contrastive learning framework that unites inter-view and intra-view objectives. Inter-view contrastive learning aligns protein sequences with their corresponding structures by maximizing mutual information, thereby capturing the consistency of protein residue order and backbone coordinates. Intra-view contrastive learning enhances the representation discriminability of each modality by explicitly separating proteins with no common location and attracting those with any shared localization. Extensive experiments demonstrate that DMVCL significantly outperforms existing baselines. Ablation studies and visualizations further highlight the contributions of domain-sequence/structure attention and multi-view contrastive learning in achieving superior predictive performance.

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EAAI Journal 2025 Journal Article

The pseudo-analytical probability solution to parameterized Fokker–Planck equations via deep learning

Xiaolong Wang
Jing Feng
Gege Wang
Tong Li
Yong Xu

Efficiently solving Fokker–Planck equations (FPEs) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions are only attainable in specific cases. To speed up the solving process of parameterized FPEs with several system parameters, we introduce a deep learning-based method to obtain the pseudo-analytical probability solution (PAPS). Unlike previous numerical methodologies that necessitate solving the FPEs separately for each set of system parameters, the PAPS simultaneously addresses all FPEs within a predefined continuous range of system parameters during a single training phase. The approach utilizes a Gaussian mixture distribution (GMD) to represent the stationary probability density functions, namely, the solutions to FPEs. By leveraging a deep residual network, each parameter configuration of the system is mapped to the parameters of the GMD, ensuring that the weights, means, and variances of Gaussian components adaptively align with the corresponding true density functions. A grid-free algorithm is further developed to effectively train the residual network, resulting in a feasible PAPS obeying nonnegativity, normalization and boundary conditions. Extensive numerical studies validate the accuracy and efficiency of our method. This approach presents new insight to the pseudo-analytical solutions to FPEs, and promises significant acceleration in the response analysis of multi-parameter, multi-dimensional stochastic nonlinear systems.

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EAAI Journal 2024 Journal Article

An improved smoking behavior detection algorithm via incorporating an interference information filtering network

Yi Li
Haojie Zhou
Jing Feng
Xing Li
Xiaobin Xu
Pingzhi Hou
Xiaomin Hu

Efficient and accurate identification of smoking behavior in public places is crucial for ensuring public health and safety. However, due to various factors like small target size, complex image background, varying cigarette angles and numerous similar objects, current methods still grapple with challenges such as missed detection and false detection when identifying cigarette targets in smoking behavior. This paper presents an enhanced version of the You Only Look Once version5-small algorithm to address these issues effectively. Firstly, to bolster the model's ability for feature extraction of cigarette targets, the Swin Transformer Block structure is integrated into the backbone network to capture long-range dependencies. Secondly, a novel Hybrid Spatial Pyramid Pooling-Fast with Cross Stage Partial Connection module is built based on the foundation of the Spatial Pyramid Pooling-Fast module by integrating both maximum pooling and average pooling to enhance the fusion ability of the multi-scale feature maps. Thirdly, a novel interference information filtering network is introduced to effectively reduce the impact of noise and confusion caused by similar objects, thus enhancing the performance of the model. According to the experimental results, the accuracy of the improved algorithm on the self-made cigarette target image data set reaches 93. 5 %, and the recall reaches 89. 1 %.

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EAAI Journal 2024 Journal Article

Interpretable detector for cervical cytology using self-attention and cell origin group guidance

Peng Jiang
Juan Liu
Jing Feng
Hua Chen
Yuqi Chen
Cheng Li
Baochuan Pang
Dehua Cao

Deep learning has advanced the development of automated cervical cytology, yet limited studies have delved into methods for incorporating medical domain knowledge, and model interpretability has not been thoroughly investigated. To address this issue, this paper proposes a novel, explainable detection method for abnormal cervical cells, called dual-stream self-attention based feature fusion and origin grouping network (DSA-FFOGNet). To encourage the model to focus more on lesion cells and cell nuclei of diagnostic significance, the dual-stream self-attention (DSA) module is introduced to enhance the learning of lesion-specific features. In view of the complex background, cell dense distribution, cell overlap, or clumps existing in the actual cervical cytology images, multi-scale features are extracted and fused by using the path aggregation network (PAN) to enhance the feature representation ability. By integrating biomedical insights regarding cell provenance and formulating an origin grouping loss, DSA-FFOGNet adjusts the penalties for cervical cells originating from different groups, thereby enhancing the optimization of the model training process. To further improve the detection performance, the classification and localization tasks are decoupled via the use of double detection heads. Extensive experiments validate the robustness of the proposed DSA-FFOGNet. The visualization of class activation maps (CAMs) showcases the model’s interpretability. The proposed approach advances the application and development of explainable artificial intelligence (XAI) models in cervical cytology and inspires further research in automated cervical cytology.

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