Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

Joint Authors

Fang, Alex Chengyu
Zhang, Xing
Yuan, Daojun
Chen, Lingling
Webster, Jonathan
Xia, Jingbo

Source

BioMed Research International

Issue

Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2013-11-25

Country of Publication

Egypt

No. of Pages

9

Main Subjects

Medicine

Abstract EN

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv.

oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach.

The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced.

Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene.

Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

American Psychological Association (APA)

Xia, Jingbo& Zhang, Xing& Yuan, Daojun& Chen, Lingling& Webster, Jonathan& Fang, Alex Chengyu. 2013. Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies. BioMed Research International،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031027

Modern Language Association (MLA)

Xia, Jingbo…[et al.]. Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies. BioMed Research International No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1031027

American Medical Association (AMA)

Xia, Jingbo& Zhang, Xing& Yuan, Daojun& Chen, Lingling& Webster, Jonathan& Fang, Alex Chengyu. Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies. BioMed Research International. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1031027

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1031027