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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 3 months ago
://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic
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the nation. The school consists of four departments with 130 tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its
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therapeutic targets. These efforts will generate large-scale, rich perturbation datasets, requiring the development of sophisticated approaches and methods incorporating facets such as machine learning and
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approach to develop chemical probes, investigate biological mechanisms, and evaluate in vivo efficacy. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR
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. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR) as models. PXR and CAR transcriptionally regulate cytochrome P450 3A4 (CYP3A4) and CYP3A5-drug
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Description As part of the Electrical Engineering program of the Engineering Division and the Center of Artificial Intelligence and Robotics at NYU Abu Dhabi the group of Prof. Kostas J