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Wenjun Shen

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Refinement Contrastive Learning of Cell–Gene Associations for Unsupervised Cell Type Identification

Liang Peng
Haopeng Liu
Yixuan Ye
Cheng Liu
Wenjun Shen
Si Wu
Hau-San Wong

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single-cell RNA-seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene-expression signatures, further validating the biological relevance of our approach.

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JBHI Journal 2025 Journal Article

Deep Self-Reinforced Multi-View Subspace Clustering for Cancer Subtyping

Cheng Liu
Baoyuan Zheng
Jiaojiao Wang
Xibiao Wang
Hang Gao
Fei Wang
Wenjun Shen
Si Wu

Identifying cancer subtypes is crucial for understanding disease progression and guiding precision medicine. With advances in high-throughput experimental technologies, the integration of multiple types of omics data for cancer subtype identification has become increasingly feasible. However, despite the promising performance of existing integrative cancer subtyping methods, efficiently integrating and clustering multi-omics datasets remains challenging due to the high levels of noise inherent in omics data, which impede the accurate characterization of relationships among samples. To address these challenges, we propose a novel deep multi-view subspace clustering model that incorporates a self-reinforced learning strategy. This strategy iteratively improves the quality of self-representation, which is critical for accurately capturing sample relationships and enabling effective clustering. Specifically, during model training, the proposed method learns a highly reliable self-representation through a good-neighbor learning mechanism, allowing it to model more accurate and robust inter-sample relationships. Building upon this reliable self-representation, we further develop a learnable view-graph fusion framework that integrates complementary information across multiple omics views to derive a consensus representation for clustering, thereby guiding the overall learning process. In addition, we introduce a local graph-guided learning mechanism based on an initial graph constructed from the raw data. This mechanism serves as an effective regularization strategy to prevent the model from converging to suboptimal solutions, thereby enhancing stability and robustness during training. Extensive experimental results demonstrate that the proposed method consistently outperforms several state-of-the-art approaches, validating its effectiveness and robustness for cancer subtype identification.

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IROS Conference 2025 Conference Paper

Sensor-Free Self-Calibration for Collaborative Robots Using Tri-Sphere End-Effector Toward High Orientation Accuracy

Jianhui He
Guilin Yang
Yiyang Feng
Jingbo Luo
Si-Lu Chen 0001
Wenjun Shen

Collaborative robots often exhibit limited absolute accuracy despite high repeatability, necessitating cost-effective calibration solutions. This paper presents a novel sensor-free self-calibration method for collaborative robots using position and distance constraints. A tri-sphere end-effector with precision balls and magnetic holders enables repeatable Tool Center Point (TCP) positioning (<0. 01mm) through hand-guiding, where the three-sphere configuration crucially enhances the orientation calibration accuracy compared to a single-sphere approach. The proposed device eliminates expensive external sensors while establishing geometric constraints through workspace-wide TCP engagements. By analyzing relative position/distance errors between multiple configurations, the method identifies kinematic parameters via a Local Product of Exponential (Local POE) based error model. Experiments demonstrated a 91. 7% position error reduction (7. 98mm to 0. 66mm) and 69. 6% orientation improvement (0. 0069rad to 0. 0021rad), achieving comparable accuracy to laser-tracker methods at <1% device cost. This approach offers a low-cost, mechanically robust solution for enhancing collaborative robot accuracy in industrial applications.

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AAAI Conference 2025 Conference Paper

SpotDiff: Spatial Gene Expression Imputation Diffusion with Single-Cell RNA Sequencing Data Integration

Tianyi Chen
Yunfei Zhang
Lianxin Xie
Wenjun Shen
Si Wu
Hau-San Wong

The advent of Spatial Transcriptomics (ST) has revolutionized understanding of tissue architecture by creating high-resolution maps of gene expression patterns. However, the low capture rate of ST leads to significant sparsity. The aim of imputation is to recover biological signals by imputing the dropouts in ST data to approximate the true expression values. In this paper, we introduce a Spatial Gene Expression Imputation Diffusion model to facilitate ST data imputation, and our model is referred to as SpotDiff. Specifically, we incorporate a spot-gene prompt learning module to capture the association between spots and genes. Further, SpotDiff integrates single-cell RNA sequencing data to impute gene expression at each spot. The proposed approach is able to reduce the uncertainty in the imputation process, since the aggregation of multiple single-cell measurements yield a stable representation of the corresponding spot expression profile. Extensive experiments have been performed to demonstrate that SpotDiff outperforms existing imputation methods across multiple benchmarks in terms of yielding more accurate and biologically relevant gene expression profiles, particularly in highly sparse scenarios.

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IROS Conference 2024 Conference Paper

A Piecewise-weighted RANSAC Method Utilizing Abandoned Hypothesis Model Information with a New Application on Robot Self-calibration

Jianhui He
Yiyang Feng
Guilin Yang
Wenjun Shen
Si-Lu Chen 0001
Tianjiang Zheng
Junjie Li

Industrial robots and collaborative robots are widely employed in industry and are progressively being utilized to assist individuals in their daily routines. To improve their absolute accuracy, self-calibration methods using portable local measurement devices are cost-effective solutions. However, compared with the conventional external calibration methods, self-calibration methods employing two configurations as a calibration sample introduce more non-kinematic errors to the robot. Therefore, noise reduction is significantly necessary in self-calibration. A novel Piecewise-weighted Random Sample Consensus (RANSAC) method is proposed in this paper. Instead of choosing an optimal model with all inliers, the proposed method employs a general weight considering both the sample and hypothesis model qualities to generate a new model with Weighted Least Square (WLS) method. Besides, the proposed method turns the target of finding an uncontaminated set of inliers into the training of the proper weight coefficient for WLS, which not only improves the accuracy but also greatly enhances the speed. The self-calibration experiment on a 6 degree-of-freedom(DOF) robot CR10 shows that the accuracy of the proposed Piecewise-weighted RANSAC method makes a 27. 7% accuracy improvement from that employing Least Square method, a 20. 0% accuracy improvement from that employing standard RANSAC method, and a 5. 5% accuracy improvement from that employing LO-RANSAC method. Besides, the proposed method is also over 10. 9 times faster than the standard RANSAC method and 18. 6 times faster than the LO-RANSAC method.

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IJCAI Conference 2024 Conference Paper

SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transformer for Gene-selective Cell Type Annotation

Lu Lin
Wen Xue
Xindian Wei
Wenjun Shen
Cheng Liu
Si Wu
Hau San Wong

Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e. g. , identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cell type from gene expression vectors, and thus fall short in discovering and associating salient genes with specific cell types. To address this issue, we propose a multi-scale scRNA-seq Sub-vector Completion Transformer, and our model is referred to as SCTrans. Considering that the expressiveness of gene sub-vectors is richer than that of individual genes, we perform multi-scale partitioning on gene vectors followed by masked sub-vector completion, conditioned on unmasked ones. Toward this end, the multi-scale sub-vectors are tokenized, and the intrinsic contextual relationships are modeled via self-attention computation and conditional contrastive regularization imposed on an encoding transformer. By performing mutual learning between the encoder and an additional lightweight counterpart, the salient tokens can be distinguished from the others. As a result, we can perform gene-selective cell type annotation, which contributes to our superior performance over state-of-the-art annotation methods.

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