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      <image:title>Blog - Liquid Biopsy Intelligence: ctDNA in the Age of AI - Make it stand out</image:title>
      <image:caption>Figure 1. ctDNA Biology and Liquid Biopsy Workflow. Tumor cells shed ctDNA into the bloodstream through apoptosis and necrosis. Following cfDNA extraction and sequencing, ctDNA analysis enables four clinical application domains: early cancer detection, MRD monitoring, precision therapy guidance, and AI/ML-assisted interpretation.</image:caption>
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      <image:title>Blog - Liquid Biopsy Intelligence: ctDNA in the Age of AI - Make it stand out</image:title>
      <image:caption>Figure 2. AI/ML Data Analysis Pipeline for ctDNA Liquid Biopsy. Raw multi-modal cfDNA inputs (mutations, methylation, fragmentomics, CNV, nucleosomics, protein markers) flow through QC pre-processing into a tiered ML model layer, yielding four categories of clinical output: cancer detection, MRD status, therapy guidance, and prognostic risk scoring.</image:caption>
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      <image:title>Blog - Spatial Transcriptomics: From Tissue to Insight - Make it stand out</image:title>
      <image:caption>A workflow diagram showing the spatial transcriptomics pipeline across five steps: tissue sectioning, mRNA capture, sequencing/imaging, alignment/QC, and AI-driven discovery. It catalogs sequencing- and imaging-based platforms, illustrates how tissue morphology maps to gene expression spot grids, and summarizes eight AI analytical task categories with representative tools.</image:caption>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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