Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature.
With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations bet...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/16/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118823538&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118823538 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/16/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118823538 118823538 118823538 10.1155/2016/3594937 118823538 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature. aug: au: Feng, Qinlin Gui, Yingyi Yang, Zhihao Wang, Lei Li, Yuxia affil: College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China sug: subj: Disease Symptoms Disease Therapy Drug Discovery Medical Literature Algorithms Information Retrieval ROC Curve Precision Descriptive Statistics kappa Statistic Funding Source ab: With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy. In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively. The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon. These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance. In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance. Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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