ArticleAmino acids2008
Prediction of mutations engineered by randomness in H5N1 neuraminidases from influenza A virus.
Article in Amino acids, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Possibility of cross-species/subtype reassortments in influenza A viruses: an analysis of nonstructural protein variations.Virulence · 2013Article
- Trends in global warming and evolution of matrix protein 2 family from influenza A virus.Interdisciplinary sciences, computational life sciences · 2009Article
- Determination of mutation patterns in human ornithine transcarbamylase precursor.Journal of clinical monitoring and computing · 2009Article
- Connecting mutant phenylalanine hydroxylase with phenylketonuria.Journal of clinical monitoring and computing · 2008Article
- Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus.Amino acids · 2008Article
- Quantitative relationship between mutated amino-acid sequence of human copper-transporting ATPases and their related diseases.Molecular diversity · 2008Article
- Accuracy of Next Generation Sequencing Platforms.Next generation, sequencing & applicationsArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this proof-of-concept study, we attempt to determine whether the cause-mutation relationship defined by randomness is protein dependent by predicting mutations in H5N1 neuraminidases from influenza A virus, because we have recently conducted several concept-initiated studies on the prediction of mutations in hemagglutinins from influenza A virus. In our concept-initiated studies, we defined the randomness as a cause for mutation, upon which we built a cause-mutation relationship, which is then switched into the classification problem because the occurrence and non-occurrence of mutations can be classified as unity and zero. Thereafter, we used the logistic regression and neural network to solve this classification problem to predict the mutation positions in hemagglutinins, and then used the amino acid mutating probability to predict the would-be-mutated amino acids. As the previous results were promising, we extend this approach to other proteins, such as H5N1 neuraminidase in this study, and further address various issues raised during the development of this approach. The result of this study confirms that we can use this cause-mutation relationship to predict the mutations in H5N1 neuraminidases.
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