PaPI: pseudo amino acid composition to score human protein-coding variants
نویسندگان
چکیده
منابع مشابه
Predicting membrane protein type by functional domain composition and pseudo-amino acid composition.
Given the sequence of a protein, how can we predict whether it is a membrane protein or non-membrane protein? If it is, what membrane protein type it belongs to? Since these questions are closely relevant to the function of an uncharacterized protein, their importance is self-evident. Particularly, with the explosion of protein sequences entering into databanks and the relatively much slower pr...
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The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246...
متن کاملUsing grey dynamic modeling and pseudo amino acid composition to predict protein structural classes
Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the PseAA composition is an important problem yet to be solved. In this article the grey modeling approach is introdu...
متن کاملPredicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.
Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2015
ISSN: 1471-2105
DOI: 10.1186/s12859-015-0554-8