Designing microplate layouts using artificial intelligence

Rodríguez, María Andreína Francisco; Carreras-Puigvert, Jordi; Spjuth, Ola

doi:10.1016/j.ailsci.2023.100073

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…At present, there are already some tools for plate design such as OSAT [ 14 ], Well Plate Maker(WPM) [ 15 ], Omixer [ 16 ], PlateDesigner [ 17 ], PlateEditor [ 18 ] and PLAID [ 19 ]. Although the plate design takes up little time in the whole experimental process, the layout design and the injection sequence of the experiment have a significant impact on the final identification.…”

Section: Resultsmentioning

confidence: 99%

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

Lu,

Jiang,

Wang

et al. 2023

BMC Bioinformatics

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Background Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. Results Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. Conclusions InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign. A free web service is also provided: http://www.injection.design.

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Section: Resultsmentioning

confidence: 99%

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

Lu,

Jiang,

Wang

et al. 2023

BMC Bioinformatics

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“…One notable advantage of generating in-house images lies in the control over parameters, including image resolution and plate layout design. In this context, they employed a tool called PLAID (Plate Layouts using Artificial Intelligence Design) ( Francisco Rodríguez et al, 2023 ) to achieve an optimized plate layout that minimizes plate effects. Other datasets from the Broad Institute, using different cell lines and treatment as gene expression can be accessed at the cell painting gallery ( Institute B, 2024 ).…”

Section: Exploring Integration Of Molecular Fingerprints and Cell Pai...mentioning

confidence: 99%

Unleashing the potential of cell painting assays for compound activities and hazards prediction

Odje,

Meijer,

von Coburg

et al. 2024

Front. Toxicol.

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The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.

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Artificial intelligence for high content imaging in drug discovery

Carreras-Puigvert,

Spjuth

2024

Current Opinion in Structural Biology

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Designing microplate layouts using artificial intelligence

Cited by 6 publications

References 42 publications

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

Unleashing the potential of cell painting assays for compound activities and hazards prediction

Artificial intelligence for high content imaging in drug discovery

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