Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.

Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression.

CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites.

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches.

A review of molecular mechanisms linked to potential renal injury agents in tropical rural farming communities.

The chronic kidney disease of unknown etiology (CKDu) is a global health concern primarily impacting tropical farming communities. Although the precise etiology is debated, CKDu is associated with environmental exposures including heat stress and chemical contaminants such as fluoride, heavy metals, and herbicide glyphosate. However, a comprehensive synthesis is lacking on molecular networks underpinning renal damage induced by these factors.