New Challenges for Biological Text-Mining in the Next Decade

Guardado en:
Detalles Bibliográficos
Publicado en:Journal of Computer Science and Technology vol. 25, no. 1 (Jan 2010), p. 169
Autor principal: Dai, Hong-Jie
Otros Autores: Chang, Yen-Ching, Tzong-Han Tsai, Richard, Hsu, Wen-Lian
Publicado:
Springer Nature B.V.
Materias:
Acceso en línea:Citation/Abstract
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 872095032
003 UK-CbPIL
022 |a 1000-9000 
022 |a 1860-4749 
024 7 |a 10.1007/s11390-010-9313-5  |2 doi 
035 |a 872095032 
045 2 |b d20100101  |b d20100131 
084 |a 137755  |2 nlm 
100 1 |a Dai, Hong-Jie  |u “Academia Sinica”, Institute of Information Science, Taiwan, China; “National Tsing-Hua University”, Department of Computer Science, Taiwan, China 
245 1 |a New Challenges for Biological Text-Mining in the Next Decade 
260 |b Springer Nature B.V.  |c Jan 2010 
513 |a Journal Article 
520 3 |a The massive flow of scholarly publications from traditional paper journals to online outlets has benefited biologists because of its ease to access. However, due to the sheer volume of available biological literature, researchers are finding it increasingly difficult to locate needed information. As a result, recent biology contests, notably JNLPBA and BioCreAtIvE, have focused on evaluating various methods in which the literature may be navigated. Among these methods, text-mining technology has shown the most promise. With recent advances in text-mining technology and the fact that publishers are now making the full texts of articles available in XML format, TMSs can be adapted to accelerate literature curation, maintain the integrity of information, and ensure proper linkage of data to other resources. Even so, several new challenges have emerged in relation to full text analysis, life-science terminology, complex relation extraction, and information fusion. These challenges must be overcome in order for text-mining to be more effective. In this paper, we identify the challenges, discuss how they might be overcome, and consider the resources that may be helpful in achieving that goal.[PUBLICATION ABSTRACT] 
653 |a Computers 
653 |a Data mining 
653 |a Computer science 
653 |a Journals 
653 |a Publishing industry 
653 |a Biological effects 
653 |a Text analysis 
653 |a Biology 
653 |a Data integration 
653 |a Academic publications 
653 |a Natural language processing 
653 |a Algorithms 
653 |a Scholarly publishing 
653 |a Genes 
653 |a Full text 
653 |a Proteins 
653 |a Terminology 
700 1 |a Chang, Yen-Ching  |u “Academia Sinica”, Institute of Information Science, Taiwan, China 
700 1 |a Tzong-Han Tsai, Richard  |u Yuan Ze University, Department of Computer Science and Engineering, Taiwan, China 
700 1 |a Hsu, Wen-Lian  |u “Academia Sinica”, Institute of Information Science, Taiwan, China; “National Tsing-Hua University”, Department of Computer Science, Taiwan, China 
773 0 |t Journal of Computer Science and Technology  |g vol. 25, no. 1 (Jan 2010), p. 169 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/872095032/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/872095032/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch