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Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
BackgroundWith the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play...
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Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
BackgroundThe pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved,...
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PDDGCN: A Parasitic Disease–Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network
The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic...
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Predicting miRNA–Disease Associations by Combining Graph and Hypergraph Convolutional Network
AbstractmiRNAs are important regulators for many crucial biological processes. Many recent studies have shown that miRNAs are closely related to...
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Drug-target interaction prediction using semi-bipartite graph model and deep learning
BackgroundIdentifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the...
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G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics
BackgroundLiquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis...
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Predicting potential microbe-disease associations based on auto-encoder and graph convolution network
The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes...
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A novel method for drug-target interaction prediction based on graph transformers model
BackgroundDrug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning....
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Graph Theory in the Biological Networks
Graph theory is a mathematical tool widely used to study many different areas today. In this chapter, we demonstrate how the basic graph theory...
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gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network
BackgroundLong non-coding RNAs (lncRNAs) are related to human diseases by regulating gene expression. Identifying lncRNA-disease associations (LDAs)...
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PyMulSim: a method for computing node similarities between multilayer networks via graph isomorphism networks
BackgroundIn bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which...
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Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network
BackgroundBrain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene...
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Predicting drug–protein interactions by preserving the graph information of multi source data
Examining potential drug–target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant...
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Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an...
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Identifying miRNA-Disease Associations Based on Simple Graph Convolution with DropMessage and Jum** Knowledge
MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for...
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Drug response prediction using graph representation learning and Laplacian feature selection
BackgroundKnowing the responses of a patient to drugs is essential to make personalized medicine practical. Since the current clinical drug response...
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Molecular characterization of novel bipartite begomovirus associated with enation leaf disease of Garden croton (Codiaeum variegatum L.)
Garden croton ( Codiaeum variegatum L.) plants showing typical begomovirus symptoms of vein twisting, enation and curling were collected from...
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Graph regularized non-negative matrix factorization with prior knowledge consistency constraint for drug–target interactions prediction
BackgroundIdentifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are...
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MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network
BackgroundMircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.assamica (CSA) through their associations...
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Graph-theoretic constraints on vesicle traffic networks
Eukaryotic cells use small membrane-enclosed vesicles to transport molecular cargo between intracellular compartments. Interactions between molecules...
