AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Databases, Protein

Showing 91 to 100 of 698 articles

Clear Filters

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

PLoS computational biology
As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in pro...

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Proteins
Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with vario...

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction.

Methods (San Diego, Calif.)
BACKGROUND: Protein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the year...

Protein embeddings predict binding residues in disordered regions.

Scientific reports
The identification of protein binding residues helps to understand their biological processes as protein function is often defined through ligand binding, such as to other proteins, small molecules, ions, or nucleotides. Methods predicting binding re...

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Scientific data
Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins...

[Not Available].

Proteomics
RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of t...

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...

DSSGNN-PPI: A Protein-Protein Interactions prediction model based on Double Structure and Sequence graph neural networks.

Computers in biology and medicine
The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein-Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel p...

Protein-Protein Interfaces: A Graph Neural Network Approach.

International journal of molecular sciences
Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computationa...

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model.

Journal of bioinformatics and computational biology
Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction ne...