AIMC Topic: Deep Learning

Clear Filters Showing 24581 to 24590 of 28423 articles

MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.

Briefings in bioinformatics
DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confirmed numerous methylation-disease associations, which provide valuable prior knowledge for advancing ...

PCLSurv: a prototypical contrastive learning-based multi-omics data integration model for cancer survival prediction.

Briefings in bioinformatics
Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due to the complex and multi-omics nature of cancer data. Existing methods often struggle to capture the comprehensive range of informative features requir...

Relational similarity-based graph contrastive learning for DTI prediction.

Briefings in bioinformatics
As part of the drug repurposing process, it is imperative to predict the interactions between drugs and target proteins in an accurate and efficient manner. With the introduction of contrastive learning into drug-target prediction, the accuracy of dr...

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.

Briefings in bioinformatics
Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit signific...

Graph neural networks for single-cell omics data: a review of approaches and applications.

Briefings in bioinformatics
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...

Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.

Briefings in bioinformatics
An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study,...

TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Briefings in bioinformatics
Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment ...

Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.

Bioinformatics (Oxford, England)
MOTIVATION: S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration ...

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

Bioinformatics (Oxford, England)
MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical out...