AI Medical Compendium Journal:
Bioanalysis

Showing 1 to 9 of 9 articles

Bioanalysis of antihypertensive drugs by LC-MS: a fleeting look at the regulatory guidelines and artificial intelligence.

Bioanalysis
Hypertension is a multifaceted cardiovascular disease, a significant risk factor for stroke, heart attack, heart failure, and renal damage. An essential phase in the drug development process is the exploration of effective bioanalytical approaches to...

Artificial intelligence and blockchain in clinical trials: enhancing data governance efficiency, integrity, and transparency.

Bioanalysis
This article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in...

Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency.

Bioanalysis
The process of developing new drugs in the pharmaceutical industry is both time-consuming and costly, making efficiency crucial. Recent advances in hardware and computational methods have led to the widespread application of computational science app...

A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood.

Bioanalysis
Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-sp...

Empowering peptidomics: utilizing computational tools and approaches.

Bioanalysis
Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biolog...

Ontology-based metabolomics data integration with quality control.

Bioanalysis
 The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. This p...

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Bioanalysis
BACKGROUND: Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.