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Discovering the Hidden Connections between Genes and Proteins from the Undiscovered Public Literature

Published: 15 August 2016 Publication History

Abstract

There are lots of literature published for bio-medical researches every day. These bio-medical documents provides lots of helpful information. However, some information is hidden in these already published literature. When collecting these papers together, the hidden connections are shown up. The goal of this paper is trying to find out the hidden connections between genes and proteins. Also, in order to improve the performance of the previous work, we design the new algorithm for the syntax analysis and negative term filtering. We also compare the works to the previous work. We further use the algorithm to discover the hidden connections between genes and the protein. The experimental results also show that the new algorithm can discovery more data and also more accurate than previous work.

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  1. Discovering the Hidden Connections between Genes and Proteins from the Undiscovered Public Literature

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    cover image ACM Other conferences
    MISNC, SI, DS 2016: Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016
    August 2016
    371 pages
    ISBN:9781450341295
    DOI:10.1145/2955129
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 15 August 2016

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    Author Tags

    1. gene relationships discovering
    2. hidden connection discovering
    3. parsing tree

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    MISNC, SI, DS 2016 Paper Acceptance Rate 57 of 97 submissions, 59%;
    Overall Acceptance Rate 57 of 97 submissions, 59%

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