Wenbin Liu

CrossBranch: cross-domain cell-type deconvolution with dual-branch representation learning

Qianbei Yi, Jiaqi Yuan, Peng Xu, Wenbin Liu

Abstract Accurate estimation of cell-type composition from mixed omics data is essential for understanding tissue heterogeneity and disease mechanisms. However, existing deconvolution methods are often affected by discrepancies between reference single-cell data and target bulk, proteomic, or spatial omics measurements. This study …

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MOEMDA: A Multi-View Graph Mixture of Experts for Predicting miRNA-Disease Associations

Peng Xu, Yanling Tan, Chi Zhou, Xiaofeng Zhang et autres

Crosstalk In: crosstalk-aware inference of tumor microenvironment cell infiltration

Qianbei Yi, Jiaqi Yuan, Zheng Ye, Peng Xu et autres

BACKGROUND: The tumor microenvironment (TME) is a complex ecosystem whose cellular composition and interactions shape tumor progression, therapeutic response, and patient prognosis. However, most bulk deconvolution methods infer cell-type composition while treating cell types independently. METHODS: In this paper, we develop CrosstalkIn, …

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SH-Imputer: Spatiotemporal Data Imputation under Sparse Historical Data for Sparse Sensing

Hao Du, Wenbin Liu, En Wang, Yumeng Liang et autres

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Primary Follicle Paces Fish Ovarian Maturation Developmental Progression via the Enhancement of Notch and mTOR

Guangjing Zhang, Xiudan Yuan, Wen Fu, Yujiao Wang et autres

Dynamic developmental states of follicles are regarded to be determinants of sexual maturation in fish ovaries. However, it is still a challenge to identify the critical points at which the developmental processes of different types of follicles interact and affect the ovarian …

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Cuproptosis-driven astrocyte reactivity exacerbates experimental cerebral malaria pathogenesis

Xinpeng Hou, Xiumei Mo, Xiaoran Zhang, Qi Wang et autres

Abstract Background Cerebral malaria (CM), a lethal neurological complication of Plasmodium falciparum , is characterized by blood–brain barrier (BBB) disruption. Although astrocytes constitute essential components of the BBB neurovascular unit, their immunoregulatory functions during CM pathogenesis remain elusive. Clinical evidence of altered …

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SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics

Jiaqi Yuan, Junhua Yu, Qizhong Yi, Ye Zheng et autres

BACKGROUND: The integration of single-cell RNA sequencing (scRNA-seq) and high-resolution spatial transcriptomics (ST) could improve our understanding of both tissue architecture and cellular heterogeneity simultaneously. The key to accomplishing this goal mainly relies on effectively co-embedding similar cells with consistent representations from …

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Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact

Xiaohan Yuan, Cheng‐Wei Chen, Zhang Shi, Wenbin Liu et autres

Pancreatic cystic neoplasms (PCN) are critical precursors for early pancreatic cancer detection, yet current diagnostic methods lack accuracy and consistency. This multicenter study developed and validated an artificial intelligence (AI)-powered CT model (PCN-AI) for improved assessment. Using contrast-enhanced CT images from 1835 …

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A virtual multi-level directory file addressing method (VMDFAM) for DNA storage

Xiangzhen Zan, Xiangyu Yao, Ling Chu, Peng Xu et autres

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Annotation-free method for breast tumour segmentation in dynamic contrast-enhanced MRI

Jiahui He, Junjie Zhang, Xu Huang, Yue Liu et autres

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Integrating CRISPR-Cas12a with Aptamer as a Logic Gate Biosensing Platform for the Detection of CD33 and CD123

Xiaolong Shi, Changjun Lv, Wenbin Liu, Fengyue Zhang et autres

Molecular logic gates, as biomolecule-based computational systems, are highly suitable for multitarget detection due to their programmability and modularity. However, existing systems are primarily limited to nucleic acid detection and have not been widely applied to disease-related sensing, particularly for disease antigens. …

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A Novel Deep Learning–based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma

Wenbin Liu, Jing Li, Xiaohan Yuan, Cheng‐Wei Chen et autres

BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole slide images and deep learning to enhance PDAC prognosis prediction. METHODS: …

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