Taosheng Xu
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Author Swipe
View article: RSD-YOLO: An improved YOLOv7-tiny framework for oat disease severity identification with integration of ReXNet and decoupled head
RSD-YOLO: An improved YOLOv7-tiny framework for oat disease severity identification with integration of ReXNet and decoupled head Open
View article: SpaViT: Self-supervised Prediction of High-Resolution Spatial Transcriptomics with Vision Transformer
SpaViT: Self-supervised Prediction of High-Resolution Spatial Transcriptomics with Vision Transformer Open
View article: Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering
Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering Open
View article: scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data Open
Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clust…
View article: stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation Open
Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a dra…
View article: ICHPro: Intracerebral Hemorrhage Prognosis Classification Via Joint-attention Fusion-based 3d Cross-modal Network
ICHPro: Intracerebral Hemorrhage Prognosis Classification Via Joint-attention Fusion-based 3d Cross-modal Network Open
Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke, necessitating timely and accurate prognostic evaluation to reduce mortality and disability. However, the multi-factorial nature and complexity of ICH make methods based sol…
View article: Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection
Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection Open
Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. Howev…
View article: Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images
Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images Open
The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accur…
View article: Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification
Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification Open
Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex …
View article: Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration
Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration Open
The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in …
View article: Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis
Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis Open
Background Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use g…
View article: Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis Open
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering. Unfortunately, the interpretation of the clustering results from NMF is difficult, especially for the high-dimensional biological data …
View article: Additional file 5 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Additional file 5 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data Open
Additional file 5. Cell-cell crosstalk networks.
View article: miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data
miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data Open
In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progress…
View article: Additional file 3 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Additional file 3 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data Open
Additional file 3. Conserved and rewired targets associated with miR-17/92 family.
View article: Additional file 4 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Additional file 4 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data Open
Additional file 4. Enrichment analysis of the rewired miRNA-mRNA modules associated with miR-17/92 family.
View article: Additional file 2 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Additional file 2 of Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data Open
Additional file 2. Conserved and rewired miRNA-mRNA regulatory networks and hub miRNAs.
View article: Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data
Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data Open
Background Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the mi…
View article: A novel single-cell based method for breast cancer prognosis
A novel single-cell based method for breast cancer prognosis Open
Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances o…
View article: Correction to: Identifying miRNA synergism using multiple-intervention causal inference
Correction to: Identifying miRNA synergism using multiple-intervention causal inference Open
View article: LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer
LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer Open
Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. He…
View article: Identifying miRNA synergism using multiple-intervention causal inference
Identifying miRNA synergism using multiple-intervention causal inference Open
Background Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA syn…
View article: MOESM3 of Identifying miRNA synergism using multiple-intervention causal inference
MOESM3 of Identifying miRNA synergism using multiple-intervention causal inference Open
Additional file 3: miRNA synergistic modules. The numbers of miRNA synergistic modules and breast cancer related miRNA synergistic modules are 361 and 72, respectively.
View article: Additional file 1 of Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction
Additional file 1 of Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction Open
The transfection data for checking the predicted results of miRNA-mRNA regulation relationships. This file should be viewed by R. (RDA 29,853 kb)
View article: MOESM6 of Identifying miRNA synergism using multiple-intervention causal inference
MOESM6 of Identifying miRNA synergism using multiple-intervention causal inference Open
Additional file 6: The comparison results of the mirSRN method.
View article: Additional file 1 of miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules
Additional file 1 of miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules Open
The used running R scripts for reproducing the results of the case study. (R 4 kb)
View article: MOESM1 of Identifying miRNA synergism using multiple-intervention causal inference
MOESM1 of Identifying miRNA synergism using multiple-intervention causal inference Open
Additional file 1: miRNA synergistic network. The miRNA synergistic network includes 702 miRNA-miRNA synergistic pairs, and 269 breast cancer related miRNA-miRNA pairs.
View article: Additional file 6 of Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction
Additional file 6 of Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction Open
The expression of matched miRNAs and mRNAs of the breast adenocarcinoma (BRCA) data set is downloaded from The Cancer Genome Atlas (TCGA). This file should be viewed by R. (RDATA 92,179 kb)
View article: MOESM2 of Identifying miRNA synergism using multiple-intervention causal inference
MOESM2 of Identifying miRNA synergism using multiple-intervention causal inference Open
Additional file 2: miRNA enrichment analysis results of the synergistic miRNAs in the identified miRNA synergistic network. In total, we obtain 444 Gene Ontology terms, 57 Pathways terms and 6 Diseases terms related to the synergistic miRN…
View article: Additional file 2 of miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules
Additional file 2 of miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules Open
The predicted miRNA sponge interactions by the 8 built-in methods. (XLSX 49 kb)