Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.ins.2022.07.031 http://scihub22266oqcxt.onion/10.1016/j.ins.2022.07.031 35855405!9279179!35855405     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Inf+Sci+(N+Y) 2022 ; 608 (ä): 1557-1571 Nephropedia Template TP {{tp|p=35855405|t=2022. GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings |pdf=|usr=}}{{35855405}} gab.com Text GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings JO: Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=35855405 #FOAvip In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task. Twit Text FOAVip GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings ????Inf Sci (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=35855405 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35855405 #FOAvip English Wikipedia <ref>{{Cite pmid|35855405}}</ref> GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings #MMPMID35855405 Xie Z; Zhu R; Liu J; Zhou G; Huang JX; Cui X Inf Sci (N Y) 2022[Aug]; 608 (ä): 1557-1571 PMID35855405show ga In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task. äGFCNet: Utilizing graph feature collection networks for coronavirus kn(epmc).pdf DeepDyve Pubget Overpricing