A5004939741- Cell Image Analysis Techniques
- Single-cell and spatial transcriptomics
- Molecular Biology Techniques and Applications
- Neurogenetic and Muscular Disorders Research
- Anomaly Detection Techniques and Applications
- Earthquake Detection and Analysis
- Technology and Human Factors in Education and Health
- Computational Physics and Python Applications
- GNSS positioning and interference
- RNA Research and Splicing
- CRISPR and Genetic Engineering
- Explainable Artificial Intelligence (XAI)
- Engineering Diagnostics and Reliability
- AI in cancer detection
- Ionosphere and magnetosphere dynamics
- Machine Learning and Data Classification
- Data-Driven Disease Surveillance
Google (United States)
2019-2024
Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. applied this to primary fibroblasts from 91 patients matched healthy controls, creating largest publicly available Painting image dataset date at 48 terabytes. use fixed weights a convolutional neural network trained on ImageNet...
Abstract The ionosphere is a layer of weakly ionized plasma bathed in Earth’s geomagnetic field extending about 50–1,500 kilometres above Earth 1 . ionospheric total electron content varies response to space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting one the largest sources error for position, navigation and timing services 2 Networks high-quality ground-based GNSS stations provide maps correct these errors, but large spatiotemporal gaps data...
The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape genetic risk factors. To overcome these challenges, researchers integrating novel data types from patients, including imaging studies capturing broadly applicable features patient-derived materials. These datasets, when combined machine learning, potentially hold power elucidate...
Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. applied this to primary fibroblasts from 91 patients matched healthy controls, creating largest publicly available Painting image dataset date at 48 terabytes. use fixed weights a convolutional neural network trained on ImageNet...