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262 results on '"Shen, Wenjun"'

10. SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.

Author

Chen, Tianyi, Wei, Xindian, Xie, Lianxin, Zhang, Yunfei, Liu, Cheng, Shen, Wenjun, Wu, Si, and Wong, Hau-San

Subjects
GENE expression, NUCLEOTIDE sequence, GENE expression profiling, GENE regulatory networks, RNA sequencing
Abstract

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction. [ABSTRACT FROM AUTHOR]

Published
2024
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14. A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI

Author

Shen, Wenjun, Chen, Liyun, Wei, Dongming, Qiao, Yuanfang, Zhan, Yiqiang, Shen, Dinggang, Wang, Qian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lian, Chunfeng, editor, Cao, Xiaohuan, editor, Rekik, Islem, editor, Xu, Xuanang, editor, and Yan, Pingkun, editor

Published
2021
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15. Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance

Author

Qiao, Yuanfang, Tao, Haoyi, Huo, Jiayu, Shen, Wenjun, Wang, Qian, Zhang, Lichi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Kia, Seyed Mostafa, editor, Habes, Mohamad, editor, Kumar, Vinod, editor, Rondina, Jane Maryam, editor, Tax, Chantal, editor, and Wolfers, Thomas, editor

Published
2021
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20. Self-calibration Method for Two DOF Cable-Driven Joint Module

Author

Zheng, Tianjiang, Wang, Yi, Yang, Guilin, Shen, Wenjun, Fang, Zaojun, Yang, Kaisheng, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Jain, Vipul, editor, Patnaik, Srikanta, editor, Popențiu Vlădicescu, Florin, editor, and Sethi, Ishwar K., editor

Published
2020
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24. A Two-Step Self-calibration Method with Portable Measurement Devices for Industrial Robots Based on POE Formula

Author

Gu, Lefeng, Yang, Guilin, Fang, Zaojun, Shen, Wenjun, Zheng, Tianjiang, Chen, Chinyin, Zhang, Chi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Haibin, editor, Liu, Jinguo, editor, Liu, Lianqing, editor, Ju, Zhaojie, editor, Liu, Yuwang, editor, and Zhou, Dalin, editor

Published
2019
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25. Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering

Author

Gao, Hang, Shen, Wenjun, Li, Rui, Liu, Cheng, and Wu, Si

Abstract

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

Published
2024
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