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This platform enables researchers and clinicians to extract meaningful insights from single-cell experiments through interactive data visualization and solid machine learning techniques for data classification and modeling. Key features include automatic embedding of new cells onto existing projections, effortless cell type annotation, and the ability to identify and explore diverse cell populations, making it an essential tool for those investigating cellular heterogeneity and gene expression in various biological contexts.

scOrange galima rasti Computer Vision Platforms kategorijos.

Panašiausi į scOrange produktai yra šie:

GenPage Paaukštintas → Parse Biosciences → CellTypist → TROVO → aimed analytics →

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scOrange Pagrindinės savybės

• Single Cell Analysis
  This feature helps researchers and doctors understand single cell experiments using easy tools for data visualization and machine learning.
• Explore Cell Diversity
  This feature lets users load data from any source, remove outlier cells, and find different groups of cells within their samples.
• Predict New Cell Types
  This feature allows users to create models that can identify cell types in new data samples based on known marker genes.
• Discover New Marker Genes
  This feature helps identify important genes that define each cell group and understand their biological roles.
• Automatic Cell Type Annotation
  This feature helps users easily group and identify different cell types in their data with the Annotator tool.
• Automatic Embedding of New Cells
  This feature allows users to quickly add new cells to an existing tSNE projection using a tool called the Annotator.

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BigOmics

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