AI Medical Compendium Journal:
Current issues in molecular biology

Showing 1 to 5 of 5 articles

Anti-Staphylococcal, Anti-Candida, and Free-Radical Scavenging Potential of Soil Fungal Metabolites: A Study Supported by Phenolic Characterization and Molecular Docking Analysis.

Current issues in molecular biology
and are recognized as causative agents in numerous diseases, and the rise of multidrug-resistant strains emphasizes the need to explore natural sources, such as fungi, for effective antimicrobial agents. This study aims to assess the in vitro anti-...

Characterization of the Impacts of Living at High Altitude in Taif: Oxidative Stress Biomarker Alterations and Immunohistochemical Changes.

Current issues in molecular biology
At high elevations, the human body experiences a number of pathological, physiological, and biochemical changes, all of which have adverse impacts on human health and organ vitality. This study aimed to investigate the alterations in the liver and ki...

A Feature Fusion Predictor for RNA Pseudouridine Sites with Particle Swarm Optimizer Based Feature Selection and Ensemble Learning Approach.

Current issues in molecular biology
RNA pseudouridine modification is particularly important in a variety of cellular biological and physiological processes. It plays a significant role in understanding RNA functions, RNA structure stabilization, translation processes, etc. To understa...

AoP-LSE: Antioxidant Proteins Classification Using Deep Latent Space Encoding of Sequence Features.

Current issues in molecular biology
It is of utmost importance to develop a computational method for accurate prediction of antioxidants, as they play a vital role in the prevention of several diseases caused by oxidative stress. In this correspondence, we present an effective computat...

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning.

Current issues in molecular biology
The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as computational assessment for a long time. Further, owing to the high-performance modeling of QSAR, machine learning methods have been de...