Alternatives to laboratory animals : ATLA
32090621
Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. testing, animal trials and clinical trials). Our group has been investigating the potential of m...
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques...
INTRODUCTION: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Mo...
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent res...
BACKGROUND: Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression d...
Journal of pharmacokinetics and pharmacodynamics
33651241
Quantitative systems pharmacology models are often highly complex and not amenable to further simulation and/or estimation analyses. Model-order reduction can be used to derive a mechanistically sound yet simpler model of the desired input-output rel...
CPT: pharmacometrics & systems pharmacology
33793093
Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm-AutoML, an open-source Py...
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials;...
Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate me...
BACKGROUND: Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.