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Dr. Nazar M. Zaki
local time: 2020-01-18 12:00 (+04:00 )
Dr. Nazar M. Zaki (Abstracts)
Titles Abstracts Details
  • Assignment of Protein Sequence to Functional Family Using Neural Network and Dempster-Shafer Theory (2003) [Updated 6 years ago]
    by Nazar M. Zaki   read the paper:

    Protein sequences classification is an important problem in molecular biology, and it has long been a goal for scientists and researchers. This paper describes an approach to data-driven discovery of sequence motif-based models using neural network classifier based on Dempster-Shafer Theory for assigning protein sequences to functional families. A training set of sequences with unknown functional family is used to capture regularities that are sufficient to assign the sequences to their respective families.

    A new adaptive pattern classifier based on neural network and Dempster?Shafer theory of evidence developed by Thierry Denoux1 is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by Basic Belief Assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the class membership of each prototype are determined by minimizing the mean squared differences between the classifier outputs and target values.

  • A Comparative Analysis of Protein Homology Detection Methods (2003) [Updated 8 years ago]
    by Nazar M. Zaki   read the paper:

    Functional annotation of new gene sequences is an important challenge for computational biology systems. While much progress has been made towards improving experimental methods for functional assignment to putative genes, most current genomic annotation methods rely on computational solutions for homology modeling via sequence or structural similarity. With the increasing number of computer methods available for protein remote homologies detection, a comparative evaluation of the methods from biological prospective is warranted. This study uses benchmark SCOP dataset to test and compare the ability of five different computational methods for protein homologies detection. The results provide insight to biologist as to usage, value, and reliability of the numerous methods available.