GNN-based Biomedical Knowledge Graph Mining in Drug Development

2022 ◽  
pp. 517-540
Author(s):  
Chang Su ◽  
Yu Hou ◽  
Fei Wang
Keyword(s):  
Drug Development ◽  
Graph Mining ◽  
Knowledge Graph ◽  
Biomedical Knowledge

Biomedical Knowledge Graph Embeddings for Personalized Medicine

2021 ◽  
pp. 584-595
Author(s):  
Joana Vilela ◽  
Muhammad Asif ◽  
Ana Rita Marques ◽  
João Xavier Santos ◽  
Célia Rasga ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Biomedical Knowledge

scholarly journals Biomedical Graph Visualizer for Identifying Drug Candidates

2020 ◽  
Author(s):  
Ashton Teng ◽  
Blanca Villanueva ◽  
Derek Jow ◽  
Shih-Cheng (Mars) Huang ◽  
Samantha N. Piekos ◽  
...  
Keyword(s):  
Drug Development ◽  
De Novo ◽  
Drug Candidate ◽  
Knowledge Graph ◽  
Major Barrier ◽  
Drug Candidates ◽  
Novel Treatments ◽  
Computational Discovery ◽  
Complex Relationships ◽  
User Friendly

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/

scholarly journals A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-world Environmental Health Observations: Application (Preprint)

10.2196/26714 ◽  
2020 ◽  
Author(s):  
Karamarie Fecho ◽  
Chris Bizon ◽  
Frederick Miller ◽  
Shepherd Schurman ◽  
Charles Schmitt ◽  
...  
Keyword(s):  
Environmental Health ◽  
Real World ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Graph System

scholarly journals Knowledge graph mining for realty domain using dependency parsing and QAT models

2021 ◽  
Vol 193 ◽  
pp. 32-41
Author(s):  
Alexander Zamiralov ◽  
Timur Sohin ◽  
Nikolay Butakov
Keyword(s):  
Graph Mining ◽  
Knowledge Graph ◽  
Dependency Parsing

scholarly journals Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding

2019 ◽  
Vol 1 (3) ◽  
pp. 238-270 ◽  
Author(s):  
Lei Ji ◽  
Yujing Wang ◽  
Botian Shi ◽  
Dawei Zhang ◽  
Zhongyuan Wang ◽  
...  
Keyword(s):  
Graph Mining ◽  
Knowledge Graph ◽  
Web Pages ◽  
Search Query ◽  
Short Text ◽  
Text Understanding ◽  
Concept Space ◽  
Semantic Concepts ◽  
Table Understanding ◽  
The Web

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.

Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention

2019 ◽  
Author(s):  
Qin Dai ◽  
Naoya Inoue ◽  
Paul Reisert ◽  
Ryo Takahashi ◽  
Kentaro Inui
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Graph ◽  
Biomedical Knowledge
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