AI Medical Compendium Topic

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

Phosphorylation

Showing 41 to 50 of 72 articles

Clear Filters

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...

Automating Complex, Multistep Processes on a Single Robotic Platform to Generate Reproducible Phosphoproteomic Data.

SLAS discovery : advancing life sciences R & D
Mass spectrometry-based phosphoproteomics holds promise for advancing drug treatment and disease diagnosis; however, its clinical translation has thus far been limited. This is in part due to an unstandardized and segmented sample preparation process...

Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation.

Journal of chemical information and modeling
Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the comp...

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...

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Journal of applied toxicology : JAT
Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is impo...

DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites.

Journal of proteome research
Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites becaus...

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...

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

Journal of translational medicine
BACKGROUND: Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

Brain pathology (Zurich, Switzerland)
People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be considered to be "resilient" to the effects of ADNC. We have previously reported lower neocortical le...

A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites.

Scientific reports
Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphoryl...