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      1. Carlos Slim Center for Health Research The Slim Center aims to bring the benefits of genomics-driven medicine to Latin America, gleaning new insights into diseases with relevance to the region.
      2. Gerstner Center for Cancer Diagnostics The Gerstner Center is developing next-generation diagnostic technology for cancer detection and tracking disease progression.
      3. Klarman Cell Observatory The Klarman Cell Observatory is systematically defining mammalian cellular circuits, how they work together to create tissues and organs, and are perturbed to cause disease.
      4. Merkin Institute for Transformative Technologies in Healthcare The Merkin Institute is supporting early-stage ideas aimed at advancing powerful technological approaches for improving how we understand and treat disease.
      5. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease This center is developing new paradigms and technologies to scale the discovery of biological mechanisms of common, complex diseases, by facilitating close collaborations between the Ó³»­´«Ã½ and the Danish research community.
      6. Eric and Wendy Schmidt Center The EWSC is catalyzing a new field of interdisciplinary research at the intersection of data science and life science, aimed at improving human health.
      7. Stanley Center for Psychiatric Research The Stanley Center aims to reduce the burden of serious mental illness by contributing new insights into pathogenesis, identifying biomarkers, and paving the way toward new treatments.
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      1. Art and science connection Explore the connection between art and science and how we bring together artists and Ó³»­´«Ã½ scientists through our artist-in-residence program, gallery exhibitions, and ongoing public conversations.
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ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis.
Dobson ETA, Cimini B, Klemm AH, Wählby C, Carpenter AE, Eliceiri KW. ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis. Curr Protoc. 2021;1(5):e89. doi:10.1002/cpz1.89
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Developing open-source software for bioimage analysis: opportunities and challenges.
Levet F, Carpenter AE, Eliceiri KW, Kreshuk A, Bankhead P, Haase R. Developing open-source software for bioimage analysis: opportunities and challenges. F1000Res. 2021;10:302. doi:10.12688/f1000research.52531.1
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Predicting cell health phenotypes using image-based morphology profiling.
Way GP, Kost-Alimova M, Shibue T, et al. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell. 2021;32(9):995-1005. doi:10.1091/mbc.E20-12-0784
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Unbiased discovery of in vivo imaging probes through in vitro profiling of nanoparticle libraries.
Kelly KA, Shaw SY, Nahrendorf M, et al. Unbiased discovery of in vivo imaging probes through in vitro profiling of nanoparticle libraries. Integr Biol (Camb). 2009;1(4):311-7. doi:10.1039/b821775k
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CellProfiler Analyst: data exploration and analysis software for complex image-based screens.
Jones TR, Kang IH, Wheeler DB, et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics. 2008;9:482. doi:10.1186/1471-2105-9-482
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Screening cellular feature measurements for image-based assay development.
Logan DJ, Carpenter AE. Screening cellular feature measurements for image-based assay development. J Biomol Screen. 2010;15(7):840-6. doi:10.1177/1087057110370895
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Image-based chemical screening.
Carpenter AE. Image-based chemical screening. Nat Chem Biol. 2007;3(8):461-5. doi:10.1038/nchembio.2007.15
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Semiautomated multiplexed quantum dot-based in situ hybridization and spectral deconvolution.
Byers RJ, Di Vizio D, O’connell F, et al. Semiautomated multiplexed quantum dot-based in situ hybridization and spectral deconvolution. J Mol Diagn. 2007;9(1):20-9. doi:10.2353/jmoldx.2007.060119
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Visualization of parameter space for image analysis.
Pretorius J, Bray MAP, Carpenter AE, Ruddle RA. Visualization of parameter space for image analysis. IEEE Trans Vis Comput Graph. 2011;17(12):2402-11. doi:10.1109/TVCG.2011.253
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Workflow and metrics for image quality control in large-scale high-content screens.
Bray MA, Fraser AN, Hasaka TP, Carpenter AE. Workflow and metrics for image quality control in large-scale high-content screens. J Biomol Screen. 2012;17(2):266-74. doi:10.1177/1087057111420292
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Ó³»­´«Ã½'s gene-editing technologies—CRISPR-Cas9, base editing, and prime editing—are being tested in more than 25 clinical trials to treat or cure leukemias, rare genetic diseases, high cholesterol, and other conditions.

NIH-funded discoveries from the Ó³»­´«Ã½ are powering nearly 20 clinical trials from companies testing new treatments for diseases like cancer and heart disease.

Ó³»­´«Ã½ developed a technology — partly supported by NIH funding —  that can detect trace amounts of cancer DNA from blood tests and help cancer patients find out their risk of disease recurrence earlier.

Using NIH funding, the Ó³»­´«Ã½â€™s Rare Genomes Project has worked with more than 1,300 families from all 50 U.S. states to diagnose rare genetic diseases.

Ó³»­´«Ã½ Clinical Labs has directly partnered with tens of thousands of cancer patients to analyze their DNA and accelerate research.

During the COVID-19 pandemic, Ó³»­´«Ã½ launched a large-scale diagnostic testing lab that processed over 37 million tests and saved state and federal programs nearly $2 billion.

The Ó³»­´«Ã½'s Cancer Dependency Map helps cancer researchers and drug developers discover therapeutic targets for new cancer treatments.

gnomAD, a large human genetic variant reference database developed by the Ó³»­´«Ã½ with NIH funding, has contributed to over 13 million genetic disease diagnoses since its launch in 2014.

Datasets generated at the Ó³»­´«Ã½ were used to train AlphaGenome, a cutting-edge AI model from Google DeepMind that predicts how genetic variants affect gene regulation.

the FDA granted accelerated approval for a lung cancer drug that was developed with Ó³»­´«Ã½ science and is for patients who otherwise had few treatment options.

David Liu and his team used NIH funding to invent precise gene-editing technologies, including one that may vastly improve access to genetic therapies for patients with rare disease.

NIH-funded Ó³»­´«Ã½ research is shedding new light on the biological roots of many diseases, including Alzheimer’s, Parkinson’s, and Huntington’s disease.

Scientists with Ó³»­´«Ã½â€™s Stanley Center for Psychiatric Research have found key genetic factors for schizophrenia and bipolar disorder.

Ó³»­´«Ã½ scientists are using AI to design new antibiotics and other drugs, predict drug toxicity, and pinpoint genes, molecules, and cells that might be causing disease.

Ó³»­´«Ã½ Clinical Labs has sequenced nearly 900,000 whole human genomes, producing, on average, one human genome sequence every three minutes.

Ó³»­´«Ã½ Clinical Labs developed a new method for genome sequencing that costs 75 percent less than existing methods.

 Ó³»­´«Ã½ Clinical Labs is the largest genome sequencing center of its kind in the world.

Ó³»­´«Ã½ Clinical Labs has partnered with MyOme and Southern Research Institute in Birmingham, Alabama to provide free genetic tests to people in Alabama.

Ó³»­´«Ã½ Clinical Labs has partnered with Mass General Brigham and Everygene to provide no-cost genetic testing to people throughout the US with cardiomyopathy, a disorder that can cause sudden cardiac death.

Ó³»­´«Ã½ Clinical labs and Mass General Brigham used data from NIH’s All of Us program to develop a genetic test that predicts risk of eight different heart conditions. This test is now available to patients.

Thanks to NIH funding, Ó³»­´«Ã½ Clinical Labs is collaborating with scientists across the U.S. to sequence DNA from tens of thousands of children with cancer and birth defects to study common biological pathways.

Ó³»­´«Ã½ Clinical Labs holds the world record for fastest DNA sequencing, completing whole genome sequencing and analysis in less than four hours at their facility in Burlington, Massachusetts.

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