AI Medical Compendium Topic

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

High-Throughput Screening Assays

Showing 41 to 50 of 202 articles

Clear Filters

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Bioinformatics (Oxford, England)
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.

Scientific reports
As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purificat...

High-throughput classification of S. cerevisiae tetrads using deep learning.

Yeast (Chichester, England)
Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent m...

Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.

Scientific reports
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new c...

Collaborative robotics to enable ultra-high-throughput IR-MALDESI.

SLAS technology
Over the last 5 years, IR-MALDESI-MS (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry) has been demonstrated for use in a range of high-throughput biochemical and cellular assays with remarkable sample acquisition ...

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

ACS applied materials & interfaces
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents nume...

Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains.

Journal of the American Chemical Society
Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of mu...

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Journal of chemical information and modeling
Nucleophilic index () as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an value exceeding 4.0 eV, rendering them potential...

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.

Biosensors & bioelectronics
The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the sing...

Machine Learning-Assisted High-Throughput Screening for Anti-MRSA Compounds.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.