GNN-based Biomedical Knowledge Graph Mining in Drug Development

Author(s):  
Chang Su ◽  
Yu Hou ◽  
Fei Wang
2021 ◽  
pp. 584-595
Author(s):  
Joana Vilela ◽  
Muhammad Asif ◽  
Ana Rita Marques ◽  
João Xavier Santos ◽  
Célia Rasga ◽  
...  

2020 ◽  
Author(s):  
Ashton Teng ◽  
Blanca Villanueva ◽  
Derek Jow ◽  
Shih-Cheng (Mars) Huang ◽  
Samantha N. Piekos ◽  
...  

1.AbstractMillions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients.Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/


10.2196/26714 ◽  
2020 ◽  
Author(s):  
Karamarie Fecho ◽  
Chris Bizon ◽  
Frederick Miller ◽  
Shepherd Schurman ◽  
Charles Schmitt ◽  
...  

2021 ◽  
Vol 193 ◽  
pp. 32-41
Author(s):  
Alexander Zamiralov ◽  
Timur Sohin ◽  
Nikolay Butakov

2019 ◽  
Vol 1 (3) ◽  
pp. 238-270 ◽  
Author(s):  
Lei Ji ◽  
Yujing Wang ◽  
Botian Shi ◽  
Dawei Zhang ◽  
Zhongyuan Wang ◽  
...  

Knowlege is important for text-related applications. In this paper, we introduce Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages. Microsoft Concept Graph is built upon Probase, a universal probabilistic taxonomy consisting of instances and concepts mined from the Web. We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures, which extract 2.7 million concepts from 1.68 billion Web pages. We then use conceptualization models to represent text in the concept space to empower text-related applications, such as topic search, query recommendation, Web table understanding and Ads relevance. Since the release in 2016, Microsoft Concept Graph has received more than 100,000 pageviews, 2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.


2019 ◽  
Author(s):  
Qin Dai ◽  
Naoya Inoue ◽  
Paul Reisert ◽  
Ryo Takahashi ◽  
Kentaro Inui

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