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  "author": "Yashrajsinh Chudasama",
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  "author": "Yashrajsinh Chudasama",
            
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      "extra_author": "Maria Torrente",
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  "maintainer": "Yashrajsinh Chudasama",
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  "maintainer": "Yashrajsinh Chudasama",
            
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  "name": 
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  "name": 
            
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redictive-models-for-interpretable-insights-into-lung-cancer-relapse",
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redictive-models-for-interpretable-insights-into-lung-cancer-relapse",
            
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  "notes": "SemDesLC predicts lung cancer relapse likelihood, 
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  "notes": "SemDesLC predicts lung cancer relapse likelihood, 
            
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providing oncologists with patient-centric and population-centric 
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providing oncologists with patient-centric and population-centric 
            
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analysis. Our approach bridge the gap and fulfill the needs of three 
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analysis. Our approach bridge the gap and fulfill the needs of three 
            
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different type of users: KG builders, analysts and consumers. This 
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different type of users: KG builders, analysts and consumers. This 
            
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repository contains all the necessary scripts and instructions to 
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repository contains all the necessary scripts and instructions to 
            
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reproduce the experiments.",
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reproduce the experiments.",
            
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    "description": "The German National Library of Science and 
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    "description": "The German National Library of Science and 
            
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Technology, abbreviated TIB, is the national library of the Federal 
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Technology, abbreviated TIB, is the national library of the Federal 
            
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Republic of Germany for all fields of engineering, technology, and the 
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Republic of Germany for all fields of engineering, technology, and the 
            
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natural sciences.",
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natural sciences.",
            
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      "description": "In the scope of experiments, we employ an 
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anonymized synthetic lung cancer benchmark that comprises clinical 
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anonymized synthetic lung cancer benchmark that comprises clinical 
            
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data extracted from heterogeneous sources such as publications, 
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data extracted from heterogeneous sources such as publications, 
            
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clinical trials, and clinical records representing patients diagnosed 
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clinical trials, and clinical records representing patients diagnosed 
            
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with lung cancer. The benchmark includes around 1200 patients with 
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with lung cancer. The benchmark includes around 1200 patients with 
            
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different medical characteristics.",
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      "name": "knowledge graphs",
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      "display_name": "machine learning",
            
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  "temporal_resolution": "",
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  "title": "Semantically Describing Predictive Models for 
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  "title": "Semantically Describing Predictive Models for 
            
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Interpretable Insights into Lung Cancer Relapse",
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Interpretable Insights into Lung Cancer Relapse",
            
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  "type": "dataset",
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  "version": "",
            
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}
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