Affiliations: Department of Physics, Institute of Science, Tianjin
University, Tianjin 30072, China, Fax: +86 22 8789 0061 | LiuHui Center for Applied Mathematics, Nankai
University and Tianjin University, Tianjin 300072, China, Email:
[email protected]
Abstract: The present paper overviews the issue on predicting the subcellular
location of a protein. Five meas-ures of extracting information from the global
sequence based on the Bayes discriminant algorithm are reviewed. 1) The
auto-correlation functions of amino acid indices along the sequence; 2) The
quasi-sequence-order approach; 3) the pseudo-amino acid composition; 4) the
unified attribute vector in Hilbert space, 5) Zp parameters extracted from the
Zp curve. The actual performance of the predictive accuracy is closely related
to the degree of similarity be-tween the training and testing sets or to the
average degree of pairwise similarity in dataset in a cross-validated study.
Many scholars considered that the current higher predictive accuracy still
cannot ensure that some available algorithms are effective in practice
prediction for the higher pairwise sequence identity of the datasets, but some
of them declared that construction of the dataset used for developing software
should base on the reality determined by the Mother Nature that some
subcellular locations really contain only a minor number of proteins of which
some even have a high percentage of sequence similarity. Owing to the
complexity of the problem itself, some very so-phisticated and special programs
are needed for both constructing dataset and improving the prediction. Anyhow
finding the target information in mature protein sequence and properly
cooperating it with sorting signals in predic-tion may further improve the
overall predictive accuracy and make the prediction into practice.
