The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations
By A. Keenan, Sherry L. Jenkins, Kathleen M. Jagodnik, Simon Koplev, Edward He, Denis Torre, Zichen Wang, Anders B. Dohlman, Moshe C. Silverstein, Alexander Lachmann and 97 more. Cell Systems, 2017
380 citations recorded by the source corpus.
The Library of Integrated Network-based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.
Subfields: Biomedical Engineering, Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6344032d7e05e3b37e1ccbd27844e226fe017bcb
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Citations in the corpus
- Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, methodology): “(2018) use the LINCS dataset (Keenan et al., 2018) of gene expressions from cells targeted by various perturbagens (i.”
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “Additionally, the library of integrated network-based cellular signature (LINCS) L1000 (https:// lincs proje ct. org/ LINCS/) [60] is another repository that contains information on the change in gene expression signatures of human cell lines when treated with different chemical compounds.”
- A white-box machine learning approach for revealing antibiotic mechanisms of action (2019, neutral): “NIH Common Fund programs such as “Library of Integrated Network-Based Cellular Signatures” (LINCS) and “Big Data to Knowledge” are providing increasingly comprehensive measurements of cellular physiology in response to genetic or small molecule perturbations (Keenan et al., 2018).”
- The BioGRID interaction database: 2019 update (2018, background): “These comprehensive maps of gene function promise to further accelerate biomedical research and drug discovery (12,13).”