AIMC Topic: Phosphorylation

Clear Filters Showing 61 to 70 of 77 articles

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed.

Methods in molecular biology (Clifton, N.J.)
In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular ...

A Pretrained ELECTRA Model for Kinase-Specific Phosphorylation Site Prediction.

Methods in molecular biology (Clifton, N.J.)
Phosphorylation plays a vital role in signal transduction and cell cycle. Identifying and understanding phosphorylation through machine-learning methods has a long history. However, existing methods only learn representations of a protein sequence se...

PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein-protein interaction information.

Bioinformatics (Oxford, England)
MOTIVATION: Phosphorylation is one of the most studied post-translational modifications, which plays a pivotal role in various cellular processes. Recently, deep learning methods have achieved great success in prediction of phosphorylation sites, but...

Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.

Methods in molecular biology (Clifton, N.J.)
Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein k...

DeepPhos: prediction of protein phosphorylation sites with deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Phosphorylation is the most studied post-translational modification, which is crucial for multiple biological processes. Recently, many efforts have been taken to develop computational predictors for phosphorylation site prediction, but m...

Capsule network for protein post-translational modification site prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A rece...

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fe...

iPTMnet: an integrated resource for protein post-translational modification network discovery.

Nucleic acids research
Protein post-translational modifications (PTMs) play a pivotal role in numerous biological processes by modulating regulation of protein function. We have developed iPTMnet (http://proteininformationresource.org/iPTMnet) for PTM knowledge discovery, ...

[Interleukin?22 promotes proliferation of fibroblast?like synoviocytes from patients with rheumatoid arthritis by inducing STAT3 phosphorylation].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: To clarify the mechanism by which interleukin?22 (IL?22) promotes the proliferation of fibroblast?like synoviocytes (FLS) from patients with rheumatoid arthritis (RA).

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. ...