English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54371/62179 (87%)
Visitors : 9058362      Online Users : 83
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTHU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    National Tsing Hua University Institutional Repository > 電機資訊學院 > 資訊工程學系 > 博碩士論文  >  利用化學與基因二分網路上的相似度來預測藥物標靶

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/86633

    Title: 利用化學與基因二分網路上的相似度來預測藥物標靶
    Authors: 謝匡晉
    Description: GH02101062620
    Date: 2014
    Keywords: 藥物標靶預測;網路傳遞;化學基因組;非負矩陣分解
    drug-target prediction;network propagation;Chemogenomics;Non-negative matrix factorization
    Abstract: 近年來藥物、人體受器蛋白質以及藥效的資訊快速累積,又因抗藥性和副作用使得單一藥物逐漸不敷使用,而新藥開發則需要面對龐大的實驗組合,因此電腦篩選(in silico prediction) 成為了藥物開發過程的一環,透過此方法可以事先篩選出較有可能產生反應的實驗組合,節省實驗時間及成本的消耗。我們提出一種方法來分析”藥物–目標蛋白質”的反應網路,藉由這樣的方法可以預測未實驗過的藥物-目標蛋白質組合是否會產生反應,以及找出藥物-目標蛋白質所擁有的底層官能基及化學結構之前的關聯性。為了方便與其他論文比較,實驗使用Yoshihiro Yamanishi(et al.,2010) 所提供的資料庫來進行實驗。最後在實驗結果中顯示,我們所提出的方法在經由適當的權重分配調整之後,可以非常接近比較的對象中最佳方法的準確度,藉由這個準確度的測試可以使我們所推論出的底層官能基及化學結構之前的關聯性有一定的可信度。
    In recently years, because the quantity of drug and human protein information increase quickly, the nova drug development has to face to large possible drug-target experiment pair. Therefore, the in silico prediction method become important step in drug development to cut down the cost and have raised much attention. Through those prediction methods that we can filter out the possible drug-target pair before actually conducting biological experiments or even human test.
    We proposed a new method based on network method and machine learning method to predict high probability interactional drug-target pair. Furthermore, we also want to find out the associations between the features of drug and features of target. We use the data integrated by Yoshihiro Yamanishi(et al.,2010) for comfortably to compare accuracy.
    In the result of experiment, we construct a bipartite network of both the chemical structure and protein domain association networks. The accuracy performance of our method approaches to that of the best method very closely. This result can provide confidence that our c association network actually helps in revealing the association between the two heterogeneous data features.
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/86633
    Source: http://thesis.nthu.edu.tw/cgi-bin/gs/hugsweb.cgi?o=dnthucdr&i=sGH02101062620.id
    Appears in Collections:[資訊工程學系] 博碩士論文

    Files in This Item:

    File SizeFormat
    GH02101062620.pdf294KbAdobe PDF235View/Open


    SFX Query


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback