Alignment using genetic programming with causal trees for identification of protein functions

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@Article{Hung:2006:NA,

• author = "Chun-Min Hung and Yueh-Min Huang and Ming-Shi Chang",
• title = "Alignment using genetic programming with causal trees for identification of protein functions",
• journal = "Nonlinear Analysis",
• year = "2006",
• volume = "65",
• number = "5",
• pages = "1070--1093",
• month = "1 " # sep,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1016/j.na.2005.09.048",
• abstract = "A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximising the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimise the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.",
• notes = "Hybrid Systems and Applications",

}

Genetic Programming entries for Chun-Min Hung Yueh-Min Huang Ming-Shi Chang

Citations