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On September 16, 2024 at 9:57:48 AM UTC, admin:
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Deleted resource Synthetic Lung Cancer KG from Semantically Describing Predictive Models for Interpretable Insights into Lung Cancer Relapse
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3 | "accrualPeriodicity": "", | 3 | "accrualPeriodicity": "", | ||
4 | "author": "Yashrajsinh Chudasama", | 4 | "author": "Yashrajsinh Chudasama", | ||
5 | "author_email": "yashrajsinh.chudasama@tib.eu", | 5 | "author_email": "yashrajsinh.chudasama@tib.eu", | ||
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9 | "defined_in": "https://doi.org/10.3233/SSW240012", | 9 | "defined_in": "https://doi.org/10.3233/SSW240012", | ||
10 | "doi": "10.57702/z26cs7i9", | 10 | "doi": "10.57702/z26cs7i9", | ||
11 | "doi_date_published": "2024-09-16", | 11 | "doi_date_published": "2024-09-16", | ||
12 | "doi_publisher": "TIB", | 12 | "doi_publisher": "TIB", | ||
13 | "doi_status": true, | 13 | "doi_status": true, | ||
14 | "domain": "https://ldm.kisski.de", | 14 | "domain": "https://ldm.kisski.de", | ||
15 | "end_date": "", | 15 | "end_date": "", | ||
16 | "extra_authors": [ | 16 | "extra_authors": [ | ||
17 | { | 17 | { | ||
18 | "extra_author": "Disha Purohit", | 18 | "extra_author": "Disha Purohit", | ||
19 | "orcid": "0000-0002-1442-335X" | 19 | "orcid": "0000-0002-1442-335X" | ||
20 | }, | 20 | }, | ||
21 | { | 21 | { | ||
22 | "extra_author": "Philipp D. Rohde", | 22 | "extra_author": "Philipp D. Rohde", | ||
23 | "orcid": "0000-0002-9835-4354" | 23 | "orcid": "0000-0002-9835-4354" | ||
24 | }, | 24 | }, | ||
25 | { | 25 | { | ||
26 | "extra_author": "Enrique Iglesias", | 26 | "extra_author": "Enrique Iglesias", | ||
27 | "orcid": "0000-0002-8734-3123" | 27 | "orcid": "0000-0002-8734-3123" | ||
28 | }, | 28 | }, | ||
29 | { | 29 | { | ||
30 | "extra_author": "Maria Torrente", | 30 | "extra_author": "Maria Torrente", | ||
31 | "orcid": "" | 31 | "orcid": "" | ||
32 | }, | 32 | }, | ||
33 | { | 33 | { | ||
34 | "extra_author": "Maria-Esther Vidal", | 34 | "extra_author": "Maria-Esther Vidal", | ||
35 | "orcid": "0000-0003-1160-8727" | 35 | "orcid": "0000-0003-1160-8727" | ||
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52 | "landing_page": "", | 52 | "landing_page": "", | ||
53 | "language": "English", | 53 | "language": "English", | ||
54 | "license_id": "cc-by", | 54 | "license_id": "cc-by", | ||
55 | "license_title": "Creative Commons Attribution", | 55 | "license_title": "Creative Commons Attribution", | ||
56 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 56 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
57 | "link_orkg": "", | 57 | "link_orkg": "", | ||
58 | "maintainer": "Yashrajsinh Chudasama", | 58 | "maintainer": "Yashrajsinh Chudasama", | ||
59 | "maintainer_email": "yashrajsinh.chudasama@tib.eu", | 59 | "maintainer_email": "yashrajsinh.chudasama@tib.eu", | ||
60 | "metadata_created": "2024-09-16T09:50:35.797885", | 60 | "metadata_created": "2024-09-16T09:50:35.797885", | ||
n | 61 | "metadata_modified": "2024-09-16T09:57:09.999903", | n | 61 | "metadata_modified": "2024-09-16T09:57:38.019867", |
62 | "name": | 62 | "name": | ||
63 | redictive-models-for-interpretable-insights-into-lung-cancer-relapse", | 63 | redictive-models-for-interpretable-insights-into-lung-cancer-relapse", | ||
64 | "notes": "SemDesLC predicts lung cancer relapse likelihood, | 64 | "notes": "SemDesLC predicts lung cancer relapse likelihood, | ||
65 | providing oncologists with patient-centric and population-centric | 65 | providing oncologists with patient-centric and population-centric | ||
66 | analysis. Our approach bridge the gap and fulfill the needs of three | 66 | analysis. Our approach bridge the gap and fulfill the needs of three | ||
67 | different type of users: KG builders, analysts and consumers. This | 67 | different type of users: KG builders, analysts and consumers. This | ||
68 | repository contains all the necessary scripts and instructions to | 68 | repository contains all the necessary scripts and instructions to | ||
69 | reproduce the experiments.", | 69 | reproduce the experiments.", | ||
n | 70 | "num_resources": 2, | n | 70 | "num_resources": 1, |
71 | "num_tags": 3, | 71 | "num_tags": 3, | ||
72 | "orcid": "0000-0003-3422-366X", | 72 | "orcid": "0000-0003-3422-366X", | ||
73 | "organization": { | 73 | "organization": { | ||
74 | "approval_status": "approved", | 74 | "approval_status": "approved", | ||
75 | "created": "2017-11-23T17:30:37.757128", | 75 | "created": "2017-11-23T17:30:37.757128", | ||
76 | "description": "The German National Library of Science and | 76 | "description": "The German National Library of Science and | ||
77 | Technology, abbreviated TIB, is the national library of the Federal | 77 | Technology, abbreviated TIB, is the national library of the Federal | ||
78 | Republic of Germany for all fields of engineering, technology, and the | 78 | Republic of Germany for all fields of engineering, technology, and the | ||
79 | natural sciences.", | 79 | natural sciences.", | ||
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86 | "title": "TIB", | 86 | "title": "TIB", | ||
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n | 99 | "created": "2024-09-16T09:55:03.925536", | n | ||
100 | "description": "In the scope of experiments, we employ an | ||||
101 | anonymized synthetic lung cancer benchmark that comprises clinical | ||||
102 | data extracted from heterogeneous sources such as publications, | ||||
103 | clinical trials, and clinical records representing patients diagnosed | ||||
104 | with lung cancer. The benchmark includes 1242 patients with different | ||||
105 | medical characteristics.", | ||||
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115 | "name": "Synthetic Lung Cancer KG", | ||||
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131 | "description": "In the scope of experiments, we employ an | 100 | "description": "In the scope of experiments, we employ an | ||
132 | anonymized synthetic lung cancer benchmark that comprises clinical | 101 | anonymized synthetic lung cancer benchmark that comprises clinical | ||
133 | data extracted from heterogeneous sources such as publications, | 102 | data extracted from heterogeneous sources such as publications, | ||
134 | clinical trials, and clinical records representing patients diagnosed | 103 | clinical trials, and clinical records representing patients diagnosed | ||
135 | with lung cancer. The benchmark includes around 1200 patients with | 104 | with lung cancer. The benchmark includes around 1200 patients with | ||
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169 | "vocabulary_id": null | 138 | "vocabulary_id": null | ||
170 | }, | 139 | }, | ||
171 | { | 140 | { | ||
172 | "display_name": "knowledge graphs", | 141 | "display_name": "knowledge graphs", | ||
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177 | }, | 146 | }, | ||
178 | { | 147 | { | ||
179 | "display_name": "machine learning", | 148 | "display_name": "machine learning", | ||
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184 | } | 153 | } | ||
185 | ], | 154 | ], | ||
186 | "temporal_resolution": "", | 155 | "temporal_resolution": "", | ||
187 | "title": "Semantically Describing Predictive Models for | 156 | "title": "Semantically Describing Predictive Models for | ||
188 | Interpretable Insights into Lung Cancer Relapse", | 157 | Interpretable Insights into Lung Cancer Relapse", | ||
189 | "type": "dataset", | 158 | "type": "dataset", | ||
190 | "url": "https://github.com/SDM-TIB/SemDesLC", | 159 | "url": "https://github.com/SDM-TIB/SemDesLC", | ||
191 | "version": "", | 160 | "version": "", | ||
192 | "version_note": "" | 161 | "version_note": "" | ||
193 | } | 162 | } |