scholarly journals HGVbaseG2P: a central genetic association database

2009 ◽  
Vol 37 (Database) ◽  
pp. D797-D802 ◽  
Author(s):  
G. A. Thorisson ◽  
O. Lancaster ◽  
R. C. Free ◽  
R. K. Hastings ◽  
P. Sarmah ◽  
...  
Keyword(s):  
Genetic Association ◽  
Genetic Association Database

scholarly journals Genetic association database

2004 ◽  
Vol 5 (2) ◽  
pp. 87-87 ◽  
Author(s):  
Nick Campbell
Keyword(s):  
Genetic Association ◽  
Genetic Association Database

scholarly journals Type 2 diabetes genetic association database manually curated for the study design and odds ratio

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Ji Eun Lim ◽  
Kyung-Won Hong ◽  
Hyun-Seok Jin ◽  
Yang Seok Kim ◽  
Hun Kuk Park ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Association ◽  
Study Design ◽  
Odds Ratio ◽  
Genetic Association Database

scholarly journals The Genetic Association Database

2004 ◽  
Vol 36 (5) ◽  
pp. 431-432 ◽  
Author(s):  
Kevin G Becker ◽  
Kathleen C Barnes ◽  
Tiffani J Bright ◽  
S Alex Wang
Keyword(s):  
Genetic Association ◽  
Genetic Association Database

scholarly journals Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

2018 ◽  
Author(s):  
Haohan Wang ◽  
Xiang Liu ◽  
Yifeng Tao ◽  
Wenting Ye ◽  
Qiao Jin ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Text Mining ◽  
Genetic Association ◽  
Complex Traits ◽  
Science Research ◽  
Biomedical Science ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Genetic Association Database ◽  
Amount Of Knowledge

The increasing amount of scientific literature in biological and biomedical science research has created a challenge in the continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this chal-lenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the flexibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative arti-ficial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current pri-mary task is to build the genetic association database between genes and complex traits of the human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effec-tiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.

Genetic association study in schizophrenia and intermediate phenotypes

2009 ◽  
Vol 42 (05) ◽  
Author(s):  
M Boxleitner ◽  
I Giegling ◽  
AM Hartmann ◽  
J Genius ◽  
A Ruppert ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Association ◽  
Genetic Association Study ◽  
Intermediate Phenotypes
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