Machine learning and computer vision approaches for phenotypic profiling
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Summary
Describing methods for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data, are provided.
- Type
- article
- Published
- 2017-01-02
- Cited by
- 156
- References
- 79
- OpenAlex
- https://openalex.org/W27940887
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27400335
Keywords
Business, Quality (philosophy), Total quality management, Sugar, Sugar industry
References
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- Discovering cluster-based local outliers
- Label-Free Detection of Neuronal Differentiation in Cell Populations Using High-Throughput Live-Cell Imaging of PC12 Cells
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- Systems-level interference strategies to decipher host factors involved in bacterial pathogen interaction: from RNAi to CRISPRi.
- Application of laboratory and digital techniques for visual enhancement during the ultrastructural assessment of cilia
- Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning
- Transport Across Natural and Modified Biological Membranes and its Implications in Physiology and Therapy
- Analysis, Recognition, and Classification of Biological Membrane Images.
- Zooming in on adipocytes: High and deep
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- Regulation of genome organization and gene expression by nuclear mechanotransduction
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis
- Shaping the Cell and the Future: Recent Advancements in Biophysical Aspects Relevant to Regenerative Medicine
- Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions
- Nuclear Mechanopathology and Cancer Diagnosis.
- Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering
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