https://lixiangchun.github.io/DECIDIA/index.html

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21  <title>DECIDIA</title>
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54      <h2>DECIDIA: Deep-learning for Early Cancer Interception and DIAgnosis</h2>
55	  <p>DECIDIA IS USED ONLY FOR RESEARCH PURPOSE. IT CANNOT BE USED IN CLINICAL DIAGNOSIS.</p>
56    </section>
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58    <section>
59      <p class='section-head'>Description</p>
60      <p>
61	  Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments require tedious data analytical procedures. 
62	  Here, we present a Deep-learning-based approach for Early Cancer Interception and DIAgnosis (DECIDIA) that can achieve 
63	  accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. 
64	  DECIDIA relies on feature representation learning of DNA fragments and weakly supervised learning for classification. 
65	  DECIDIA achieved high classification performance in diagnosis of cancer and cancer-type classification without the requirement of reads mapping.
66      </p>
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68	  <p>
69      We provided DECIDIA for diagnosis of colorectal cancer (CRC) and cancer-type prediction (i.e. colorectal cancer vs. 
70	  hepatocellular carcinoma vs. lung cancer). DECIDIA expects a text input file of sequencing reads one per line. 
71	  For example, you can put the following reads in a text file <b>read.txt</b> and upload the file. 
72	  DECIDIA runs prediction on your local browser by using <a href="https://onnxruntime.ai/docs/tutorials/web/">onnxruntime-web</a>. 
73	  Therefore, it's safe that data never leaves the device for inferencing. 
74	  It will take a few seconds to cache the model locally for the first time.
75	  </p>
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83    </section>
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85    <section>
86      <p class='section-head'>Classifier</p>
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88	    <select id="ClassifierID">
89	      <option>colorectal-cancer-diagnosis</option>
90		  <option>cancer-type-classification</option>
91		</select>
92	  </div>
93    </section>
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96    <section>
97      <p class='section-head'>Upload A File</p>
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103      <p class='section-head'>Predictions</p>
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