Abstract
The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Nucleotide content is a multiple utility in bioinformatics details analysis. The single nucleotide statistics method based on nucleotide content can help extract features with higher separability and make decision. In this paper, a human promoter recognition method based on multiple gene features and multilayer decision, which is called MD-MSVMs, is proposed. In our method, we firstly perform single nucleotide analysis and divide the gene set into two parts. Secondly, the multiple gene features are extracted from each part, including CpG-island, n-mer and rigidity. And then, based on multiple features, multiple support vector machines and multilayer decision model are combined to construct a human promoter recognition framework, which is flexible and can integrate new feature extraction or new classification models freely. Experimental result shows that our method has better performance and helps understanding multiple features integrating.
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Acknowledgment
This work was supported by the grants of the National Science Foundation of China, Nos. 31571364, U1611265, 61532008, 61672203, 61402334, 61472282, 61520106006, 61472280, 61472173, 61572447, 61373098 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646.
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Xu, W., Bao, W., Yuan, L., Jiang, Z. (2017). MD-MSVMs: A Human Promoter Recognition Method Based on Single Nucleotide Statistics and Multilayer Decision. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_47
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DOI: https://doi.org/10.1007/978-3-319-63309-1_47
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