ArticlePloS one2011
Integrating overlapping structures and background information of words significantly improves biological sequence comparison.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Association of magnesium intake with sleep duration and sleep quality: findings from the CARDIA study.Sleep · 2022Article
- SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform.BMC bioinformatics · 2018Article
- Alignment-free genetic sequence comparisons: a review of recent approaches by word analysis.Briefings in bioinformatics · 2014Review
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Authors and funding
6 authors.
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Abstract
Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models.
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