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-<!--Get the samples from https://www.adobe.com/go/pdfembedapi_samples-->
-<!DOCTYPE html>
-<html>
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- <title>Adobe Document Services PDF Embed API Sample</title>
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- <div id="adobe-dc-view"></div>
- <script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
- <script type="text/javascript">
-    document.addEventListener("adobe_dc_view_sdk.ready", function()
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-        var adobeDCView = new AdobeDC.View({clientId: "<YOUR_CLIENT_ID>", divId: "adobe-dc-view"});
-        adobeDCView.previewFile(
-       {
-          content:   {location: {url: "https://documentcloud.adobe.com/view-sdk-demo/PDFs/Bodea Brochure.pdf"}},
-          metaData: {fileName: "Bodea Brochure.pdf"}
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-    });
- </script>
-</body>
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 # Ikarus
 Ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. Ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts. 
 
