A text mining approach to detect mentions of protein glycosylation in biomedical text

被引:0
|
作者
Shukla, Daksha [1 ]
Jayaraman, Valadi K. [2 ]
机构
[1] Univ Pune, Bioinformat Ctr, Pune, Maharashtra, India
[2] Univ Pune, Ctr Dev Adv Comp, Pune, Maharashtra, India
关键词
Text mining; Glycosylation; Rule-based approach; Dictionary -based approach;
D O I
10.6026/97320630008758
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Protein Glycosylation is an important post translational event that plays a pivotal role in protein folding and protein is trafficking. We describe a dictionary based and a rule based approach to mine 'mentions' of protein glycosylation in text. The dictionary based approach relies on a set of manually curated dictionaries specially constructed to address this task. Abstracts are then screened for the 'mentions' of words from these dictionaries which are further scored followed by classification on the basis of a threshold. The rule based approaches also relies on the words in the dictionary to arrive at the features which are used for classification. The performance of the system using both the approaches has been evaluated using a manually curated corpus of 3133 abstracts. The evaluation suggests that the performance of the Rule based approach supersedes that of the Dictionary based approach.
引用
收藏
页码:758 / 762
页数:5
相关论文
共 50 条
  • [1] Biomedical Text Mining: Experience and Practical Approach
    Ryu, Keun Ho
    3RD INTERNATIONAL CONFERENCE ON APPLIED COMPUTING AND INFORMATION TECHNOLOGY (ACIT 2015) 2ND INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND INTELLIGENCE (CSI 2015), 2015, : 1 - 1
  • [2] Analysis of protein/protein interactions through biomedical literature: Text mining of abstracts vs. text mining of full text articles
    Martin, EPG
    Bremer, EG
    Guerin, MC
    DeSesa, C
    Jouve, O
    KNOWLEDGE EXPLORATION IN LIFE SCIENCE INFORMATICS, PROCEEDINGS, 2004, 3303 : 96 - 108
  • [3] Biomedical Text Mining Using a Grid Computing Approach
    Castellano, Marcello
    Mastronardi, Giuseppe
    Decataldo, Giacinto
    Pisciotta, Luca
    Tarricone, Gianfranco
    Cariello, Lucia
    Bevilacqua, Vitoantonio
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2008, 5227 : 1077 - 1084
  • [4] nala: text mining natural language mutation mentions
    Cejuela, Juan Miguel
    Bojchevski, Aleksandar
    Uhlig, Carsten
    Bekmukhametov, Rustem
    Karn, Sanjeev Kumar
    Mahmuti, Shpend
    Baghudana, Ashish
    Dubey, Ankit
    Satagopam, Venkata P.
    Rost, Burkhard
    BIOINFORMATICS, 2017, 33 (12) : 1852 - 1858
  • [5] Text mining the biomedical literature
    Pertsemlidis, A
    BIOPHYSICAL JOURNAL, 2002, 82 (01) : 168A - 168A
  • [6] Oddpub – a text-mining algorithm to detect data sharing in biomedical publications
    Riedel, Nico
    Kip, Miriam
    Bobrov, Evgeny
    Data Science Journal, 2020, 19 (01): : 1 - 14
  • [7] Using personal writings to detect dementia: A text mining approach
    Asllani, Beni
    Mullen, Deborah M.
    HEALTH INFORMATICS JOURNAL, 2023, 29 (04)
  • [8] Status of text-mining techniques applied to biomedical text
    Erhardt, RAA
    Schneider, R
    Blaschke, C
    DRUG DISCOVERY TODAY, 2006, 11 (7-8) : 315 - 325
  • [9] @Note: A workbench for Biomedical Text Mining
    Lourenco, Analia
    Carreira, Rafael
    Carneiro, Sonia
    Maia, Paulo
    Glez-Pena, Daniel
    Fdez-Riverola, Florentino
    Ferreira, Eugenio C.
    Rocha, Isabel
    Rocha, Miguel
    JOURNAL OF BIOMEDICAL INFORMATICS, 2009, 42 (04) : 710 - 720
  • [10] Biomedical Text Mining and Its Applications
    Rodriguez-Esteban, Raul
    PLOS COMPUTATIONAL BIOLOGY, 2009, 5 (12)