ArticleBioMed research international2013
Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Integrative pathway and network analysis provide insights on flooding-tolerance genes in soybean.Scientific reports · 2023Article
- An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean.Frontiers in plant science · 2022Article
- Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Frontiers in genetics · 2020Article
- Identification, Characteristics and Mechanism of 1-Deoxy-N-acetylglucosamine from Deep-Sea Virgibacillus dokdonensis MCCC 1A00493.Marine drugs · 2018Article
- A novel feature selection strategy for enhanced biomedical event extraction using the Turku system.BioMed research international · 2014Article
- The functional role of long non-coding RNAs and epigenetics.Biological procedures online · 2014Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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