Linlin Zhuo
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View article: A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships
A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships Open
Our findings demonstrate that RKGATMDA effectively predicts microbe-disease associations, with at least 9 out of the top 10 prediction pairs validated by biological evidence. This highlights the potential of RKGATMDA as a valuable tool in …
View article: GH-UNet: group-wise hybrid convolution-VIT for robust medical image segmentation
GH-UNet: group-wise hybrid convolution-VIT for robust medical image segmentation Open
Medical image segmentation is vital for accurate diagnosis. While U-Net-based models are effective, they struggle to capture long-range dependencies in complex anatomy. We propose GH-UNet, a Group-wise Hybrid Convolution-ViT model within t…
View article: GH-UNet: Group-wise Hybrid Convolution-VIT for Robust Medical Image Segmentation
GH-UNet: Group-wise Hybrid Convolution-VIT for Robust Medical Image Segmentation Open
Medical image segmentation is essential for accurate diagnosis and effective treatment. Although U-Net-based architectures have shown outstanding performance in this domain, they are limited in effectively capturing long-range contextual r…
View article: AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection
AnomalGRN: deciphering single-cell gene regulation network with graph anomaly detection Open
View article: Internal electric field steering S-scheme charge transfer in ZnIn2S4/COF boosts H2O2 photosynthesis from water and air for sustainable disinfection
Internal electric field steering S-scheme charge transfer in ZnIn2S4/COF boosts H2O2 photosynthesis from water and air for sustainable disinfection Open
The global need for clean water and sanitation drives the development of eco-friendly and efficient water treatment technologies to combat biological pollution from pathogens. In this study, a novel heterojunction photocatalyst was synthes…
View article: Mask Autoencoders are Semantic Segmentation Datasets Augmenter
Mask Autoencoders are Semantic Segmentation Datasets Augmenter Open
View article: Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction
Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction Open
Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease inte…
View article: MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs
MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs Open
The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial intelligence models are limited by sparse observ…
View article: DrugReAlign: a multisource prompt framework for drug repurposing based on large language models
DrugReAlign: a multisource prompt framework for drug repurposing based on large language models Open
View article: ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification
ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification Open
The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for i…
View article: GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping
GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remapping Open
Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks, a form of deep learning technology, accurately model the structure of com…
View article: Revisiting drug–protein interaction prediction: a novel global–local perspective
Revisiting drug–protein interaction prediction: a novel global–local perspective Open
Motivation Accurate inference of potential drug–protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI pr…
View article: Fusion of multi-source relationships and topology to infer lncRNA-protein interactions
Fusion of multi-source relationships and topology to infer lncRNA-protein interactions Open
View article: MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning
MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning Open
Motivation Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal …
View article: Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization Open
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistan…
View article: Joint masking and self-supervised strategies for inferring small molecule-miRNA associations
Joint masking and self-supervised strategies for inferring small molecule-miRNA associations Open
Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting …
View article: An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning
An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning Open
Multiple studies have demonstrated that microRNA (miRNA) can be deeply involved in the regulatory mechanism of human microbiota, thereby inducing disease. Developing effective methods to infer potential associations between microRNAs (miRN…
View article: StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning
StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning Open
Background DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a m…
View article: Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs
Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs Open
Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the mo…
View article: StableDNAm: Towards a Stable and Efficient Model for Predicting DNA Methylation Based on Adaptive Feature Correction Learning
StableDNAm: Towards a Stable and Efficient Model for Predicting DNA Methylation Based on Adaptive Feature Correction Learning Open
Background: DNA methylation, instrumental in numerous life processes, underscores the paramount importance of its accurate prediction. Recent studies suggest that deep learning, due to its capacity to extract profound insights, provides a …
View article: GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning
GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning Open
Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug t…
View article: Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders
Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders Open
MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to pr…
View article: Additional file 1 of StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning
Additional file 1 of StableDNAm: towards a stable and efficient model for predicting DNA methylation based on adaptive feature correction learning Open
Additional file 1.
View article: DPB-NBFnet: Using neural Bellman-Ford networks to predict DNA-protein binding
DPB-NBFnet: Using neural Bellman-Ford networks to predict DNA-protein binding Open
DNA is a hereditary material that plays an essential role in micro-organisms and almost all other organisms. Meanwhile, proteins are a vital composition and principal undertaker of microbe movement. Therefore, studying the bindings between…
View article: Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning
Predicting ncRNA–protein interactions based on dual graph convolutional network and pairwise learning Open
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA–proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological…
View article: Prediction of lncRNA–Protein Interactions via the Multiple Information Integration
Prediction of lncRNA–Protein Interactions via the Multiple Information Integration Open
The long non-coding RNA (lncRNA)–protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the pr…
View article: MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression
MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression Open
With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, tradit…
View article: HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy
HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy Open
Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved …