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    <loc>https://arcas.ai/blog</loc>
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    <lastmod>2021-09-22</lastmod>
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    <loc>https://arcas.ai/blog/deep-learning-based-cancer-patient-stratification</loc>
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    <lastmod>2021-05-17</lastmod>
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      <image:title>Blog - Deep learning-based cancer patient stratification</image:title>
      <image:caption>Figure 5: Arcas platform improves survival prediction over using clinical features.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1615981285438-RYYDOG95I1AT0O2S9F9Y/CRCNewSubtypes.jpg</image:loc>
      <image:title>Blog - Deep learning-based cancer patient stratification</image:title>
      <image:caption>Figure 4: Refined subtypes for colorectal cancer. Green and Blue dots represents CMS2 subtype, however separating this subtype into 2 subtypes as suggested by latent factor clustering makes more sense in terms of different survival characteristics of these two groups. The plot obtained from https://pubmed.ncbi.nlm.nih.gov/31792061/</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1615980955282-UF8TP3NHSZRXPJXSWQKF/Arcas_inputOutput_simpler.png</image:loc>
      <image:title>Blog - Deep learning-based cancer patient stratification</image:title>
      <image:caption>Figure 2: Data integration for cancer omics can deliver molecular patterns predictive of clinical variables.</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1615980756091-83TUSWTVQHOZA65IIF52/problemDrugDev.png</image:loc>
      <image:title>Blog - Deep learning-based cancer patient stratification</image:title>
      <image:caption>Figure 1: Drug development process and the use of genomics data</image:caption>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1615982563887-7M47AE1NDU7E2DB5QCE2/CMSpredictionAccu.jpg</image:loc>
      <image:title>Blog - Deep learning-based cancer patient stratification</image:title>
      <image:caption>Figure 3: Predicting subtypes using latent factors obtained via deep learning is more accurate. Left, accuracy in comparison to other toos. Right, Representation of colorectal tumors by reducing latent factors to 2D. The plot obtained from https://pubmed.ncbi.nlm.nih.gov/31792061/.</image:caption>
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  <url>
    <loc>https://arcas.ai/blog/redefining-cancer-subtypes-using-multi-omics-and-deep-learning</loc>
    <changefreq>monthly</changefreq>
    <priority>0.5</priority>
    <lastmod>2021-06-09</lastmod>
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      <image:title>Blog - Redefining cancer subtypes using multi-omics and deep learning - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <loc>https://arcas.ai/blog/using-artificial-intelligence-to-find-the-best-tumor-models-for-cancer-drug-screening</loc>
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    <priority>0.5</priority>
    <lastmod>2021-09-22</lastmod>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1632297060956-R33QHA220C1AO50C0T4N/0_kXWehxtGjpGsMdKP.png</image:loc>
      <image:title>Blog - Using artificial intelligence to find the best tumor models for cancer drug screening - Make it stand out</image:title>
      <image:caption>Figure 1: Showing clustering based on molecular features of cell lines (CCLE: marked red on the left plot) and PDX (marked blue on the left plot). The right plot is identical to the left one just colored by the tumor origin of the samples.</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1632297634022-GFVDB89NTM5HT0191DL4/unsplash-image-pwcKF7L4-no.jpg</image:loc>
      <image:title>Blog - Using artificial intelligence to find the best tumor models for cancer drug screening - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5daed3278ea34a7aeca939a2/1632297110688-C6H0O439NJZU0L6GZKJ1/0_-qBFBBcBjpick2K6.png</image:loc>
      <image:title>Blog - Using artificial intelligence to find the best tumor models for cancer drug screening - Make it stand out</image:title>
      <image:caption>Figure 2: Showing clustering based on molecular features of cell lines (CCLE: marked red on the left plot) and PDX (marked blue on the left plot). Right plot is identical to the left one just colored by tumor origin of the samples.</image:caption>
    </image:image>
  </url>
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    <loc>https://arcas.ai/blog/category/useCase</loc>
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  <url>
    <loc>https://arcas.ai/about-us</loc>
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    <priority>0.75</priority>
    <lastmod>2024-07-31</lastmod>
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      <image:title>About us - Altuna Akalin</image:title>
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      <image:title>About us - Bora Uyar</image:title>
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      <image:title>About us - Jonathan Ronen</image:title>
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      <image:title>About us - Vedran Franke</image:title>
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      <image:title>About us - Prof. Chris Mason (Genomics)</image:title>
      <image:caption>Prof. Chris Mason Genomics</image:caption>
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      <image:title>About us - Dr. Elena Diez (Business Dev)</image:title>
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      <image:title>About us - Prof. David Capper (Pathologist) Prof. Angelika Eggert (Oncologist)</image:title>
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      <image:title>About us - Prof. Nikolaus Rajewsky (Genomics)</image:title>
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      <image:title>About us - Precision Oncology Powered by AI and Omics</image:title>
      <image:caption>Arcas provides a cost-efficient and more precise drug discovery and diagnostics pipeline for oncology</image:caption>
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      <image:title>About us</image:title>
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    <loc>https://arcas.ai/contact-1</loc>
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    <lastmod>2019-10-30</lastmod>
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    <loc>https://arcas.ai/imprint</loc>
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    <lastmod>2019-10-30</lastmod>
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    <loc>https://arcas.ai/privacypolicy</loc>
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    <lastmod>2019-11-04</lastmod>
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    <loc>https://arcas.ai/crc-explore</loc>
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    <lastmod>2021-12-24</lastmod>
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