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

Haohan Wang, Xiang Liu, Yifeng Tao, Wenting Ye, Qiao Jin, William W. Cohen, Eric P. Xing
doi: https://doi.org/10.1101/434803
Haohan Wang
1Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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Xiang Liu
2Chinese University of Hong Kong Shenzhen, China
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Yifeng Tao
3Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
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Wenting Ye
1Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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Qiao Jin
4Tsinghua University Beijing, China
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William W. Cohen
5Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
6Google AI, Pittsburgh, PA, USA
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Eric P. Xing
5Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
7Pettum Inc., Pittsburgh, PA, USA
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Abstract

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.

Footnotes

  • ↵E-mail: haohanw{at}cs.cmu.edu

  • ‡ The work is done while the author is at CMU.

  • e http://www.genamap.org/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 05, 2018.
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Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning
Haohan Wang, Xiang Liu, Yifeng Tao, Wenting Ye, Qiao Jin, William W. Cohen, Eric P. Xing
bioRxiv 434803; doi: https://doi.org/10.1101/434803
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Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning
Haohan Wang, Xiang Liu, Yifeng Tao, Wenting Ye, Qiao Jin, William W. Cohen, Eric P. Xing
bioRxiv 434803; doi: https://doi.org/10.1101/434803

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