Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Cell Painting"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Plastic components are essential in the pharmaceutical industry, encompassing container closure systems, laboratory handling equipment, and single-use systems. As part of their material qualification process, studies on interactions between
Externí odkaz:
https://doaj.org/article/a9e48b2bcc5e4cc58888ef316ed17922
Autor:
Floriane Odje, David Meijer, Elena von Coburg, Justin J. J. van der Hooft, Sebastian Dunst, Marnix H. Medema, Andrea Volkamer
Publikováno v:
Frontiers in Toxicology, Vol 6 (2024)
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 tox
Externí odkaz:
https://doaj.org/article/db08b0896cf645dfbf1580b3e2b0f380
Publikováno v:
Artificial Intelligence in the Life Sciences, Vol 5, Iss , Pp 100098- (2024)
Having access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for variou
Externí odkaz:
https://doaj.org/article/082ca80eff354ddbabab8cab2a8af90d
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 595-598 (2023)
Fluorescence microscopy based cell painting technique profiles the morphological characteristics of specific cell organelles with high resolution. However, photo toxicity, photo bleaching and advanced instrumentation limits its utility for comprehens
Externí odkaz:
https://doaj.org/article/f6745a3b01784d70ba657fa24f4ce820
Autor:
Martin Cottet, Yuniel Fernandez Marrero, Simon Mathien, Karine Audette, Raphaelle Lambert, Eric Bonneil, Kenneth Chng, Alex Campos, David W. Andrews
Publikováno v:
SLAS Discovery, Vol 29, Iss 3, Pp 100121- (2024)
High-content imaging approaches, in combination with the use of perturbing agents such as small molecules or CRISPR-driven gene editing, have widely contributed to the identification of new therapeutic compounds. Thanks to recent advances in image-an
Externí odkaz:
https://doaj.org/article/5564449d6c78415e9c5915e263f7b2df
Autor:
Srijit Seal, Hongbin Yang, Maria-Anna Trapotsi, Satvik Singh, Jordi Carreras-Puigvert, Ola Spjuth, Andreas Bender
Publikováno v:
Journal of Cheminformatics, Vol 15, Iss 1, Pp 1-16 (2023)
Abstract The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed si
Externí odkaz:
https://doaj.org/article/e1f6430fd7444ba5b221a6795ab8bd50
Publikováno v:
SLAS Discovery, Vol 28, Iss 3, Pp 111-117 (2023)
Recent advances in automated microscopy and image analysis enables quantitative profiling of cellular phenotypes (Cell Painting). It paves the way for studying the broad effects of chemical perturbations on biological systems at large scale during le
Externí odkaz:
https://doaj.org/article/3b1c372133df40018f77f62c05eb9ec2
Publikováno v:
SLAS Discovery, Vol 28, Iss 3, Pp 53-64 (2023)
Various sources of information can be used to better understand and predict compound activity and safety-related endpoints, including biological data such as gene expression and cell morphology. In this review, we first introduce types of chemical, i
Externí odkaz:
https://doaj.org/article/f1f38259044f4a3ebd9b152a2a5aa081
Autor:
Megan E Kelley, Adi Y Berman, David R Stirling, Beth A Cimini, Yu Han, Shantanu Singh, Anne E Carpenter, Tarun M Kapoor, Gregory P Way
Publikováno v:
eLife, Vol 12 (2023)
Drug resistance is a challenge in anticancer therapy. In many cases, cancers can be resistant to the drug prior to exposure, that is, possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer ce
Externí odkaz:
https://doaj.org/article/cfac481b631b4e0093cc24ca0e9a858b
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