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Binding Sites

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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Nature communications
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identif...

The use of knowledge management tools in viroinformatics. Example study of a highly conserved sequence motif in Nsp3 of SARS-CoV-2 as a therapeutic target.

Computers in biology and medicine
Knowledge management tools that assist in systematic review and exploration of scientific knowledge generally are of obvious potential importance in evidence based medicine in general, but also to the design of therapeutics based on the protein subse...

Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...

Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model.

Analytical biochemistry
Information embedded in ligand-binding residues (LBRs) of proteins is important for understanding protein functions. How to accurately identify the potential ligand-binding residues is still a challenging problem, especially only protein sequence is ...

Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

Molecular omics
MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA s...

Using machine learning to improve ensemble docking for drug discovery.

Proteins
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activ...

GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

Cells
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling...

CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.

Scientific reports
ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computation...