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A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis
Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary...
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LncRNA-Disease Association Prediction Based on Integrated Application of Matrix Decomposition and Graph Contrastive Learning
Investigating the potential associations between long non-coding RNAs (lncRNAs) and diseases is crucial for advancing disease research and the...
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Interpreting image texture metrics applied to landscape gradient data
ContextPattern metrics drawn from image processing and remote sensing have been applied as descriptors of the texture of landscape gradient data....
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A Graph Transformer-Based Method for Predicting LncRNA-Disease Associations Using Matrix Factorization and Automatic Meta-Path Generation
LncRNAs are crucial regulators of gene expression that exert their influence on diverse cellular processes. Exploring the potential connections...
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Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics
BackgroundGenomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning...
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Hessian Regularized \(L_{2,1}\)-Nonnegative Matrix Factorization and Deep Learning for miRNA–Disease Associations Prediction
AbstractSince the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of...
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GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization
Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good...
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DNRLCNN: A CNN Framework for Identifying MiRNA–Disease Associations Using Latent Feature Matrix Extraction with Positive Samples
Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to...
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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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GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations
BackgroundA growing body of researches indicate that the disrupted expression of long non-coding RNA (lncRNA) is linked to a range of human...
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MSV: a modular structural variant caller that reveals nested and complex rearrangements by unifying breakends inferred directly from reads
Structural variant (SV) calling belongs to the standard tools of modern bioinformatics for identifying and describing alterations in genomes....
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Prediction of Potential MicroRNA–Disease Association Using Kernelized Bayesian Matrix Factorization
MicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18–22 nucleotides in length and make...
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Predicting drug characteristics using biomedical text embedding
BackgroundDrug–drug interactions (DDIs) are preventable causes of medical injuries and often result in doctor and emergency room visits. Previous...
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MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations
BackgroundMicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human...
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STEM enables map** of single-cell and spatial transcriptomics data with transfer learning
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology...
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Double matrix completion for circRNA-disease association prediction
BackgroundCircular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown...
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Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance
BackgroundDimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data....
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CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
BackgroundThe existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of...
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Chromatin 3D structure reconstruction with consideration of adjacency relationship among genomic loci
BackgroundChromatin 3D conformation plays important roles in regulating gene or protein functions. High-throughout chromosome conformation capture...
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Prediction of Virus-Receptor Interactions Based on Similarity and Matrix Completion
Viral infectious diseases are threatening human health and global security by rapid transmission and severe fatalities. The receptor-binding is the...