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High-Throughput Screening Assays

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Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.

Journal of biomedical science
The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising a...

High-content screening (HCS) workflows for FAIR image data management with OMERO.

Scientific reports
High-content screening (HCS) for bioimaging is a powerful approach to studying biological processes, enabling the acquisition of large amounts of images from biological samples. However, it generates massive amounts of metadata, making HCS experiment...

High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.

The Analyst
Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals an...

Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products.

Journal of hazardous materials
A paradigm of chemical risk assessment is continuously extending from focusing on 'single substances' to more comprehensive approaches that examines the combined toxicity among different components in 'mixtures.' This change aims to account for the c...

Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening.

Computers in biology and medicine
BACKGROUND: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects ...

Evolving biomaterials design from trial and error to intelligent innovation.

Acta biomaterialia
The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental con...

Engineering highly active nuclease enzymes with machine learning and high-throughput screening.

Cell systems
Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged fitness landscape and costly experiments. In this work, we present TeleProt, a machine learning (ML) ...

Effect of Cell-Cell Interaction on Single-Cell Behavior Revealed by a Deep Learning-Aided High-Throughput Addressable Single-Cell Coculture System.

Analytical chemistry
Cell-cell interactions are crucial for understanding various physiological and pathological processes, yet conventional population-level methods fail to disclose the heterogeneity at a single-cell resolution. Single-cell coculture systems that isolat...

Optimal structural characteristics of osteoinductivity in bioceramics derived from a novel high-throughput screening plus machine learning approach.

Biomaterials
Osteoinduction is an important feature of the next generation of bone repair materials. But the key structural factors and parameters of osteoinductive scaffolds are not yet clarified. This study leverages the efficiency of high-throughput screening ...

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

Pharmaceutical research
OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. ...