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Articles by Safaai Deris
Total Records ( 3 ) for Safaai Deris
  Saad Osman Abdalla Subair , Safaai Deris and Mohd Saberi Mohamad
  Advances in molecular biology in the last few decades and the availability of equipment in this field lead to the rapid sequencing of considerable genomes of several species. These large genome sequencing projects generate huge number of protein sequences in their primary structures that are difficult for conventional molecular biology laboratory techniques like X-ray crystallography and NMR to determine their corresponding 3D structures. Protein secondary structure prediction is a fundamental step in determining the 3D structure of a protein. In this study a new method for predicting protein secondary structure from amino acid sequences has been proposed and implemented. The prediction method was analyzed together with other five well known prediction methods in this domain to allow easy comparison and clear conclusions. Cuff and Barton 513 protein data set was used in training and testing the prediction methods under the same hardware, platforms and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary structures classes. The newly developed method (NN-GORV) was rigorously tested together with the other methods and observed outperformed the GOR-V methods by 7.4% Q3 and the neural networks method (NN-II) by 5.6% Q3 accuracy. The Mathews Correlation Coefficients (MCC) showed that NN-GORV secondary structure predicted states are strongly related to the observed secondary structure states.
  Saad Osman Abdalla and Safaai Deris
  Novel researchers in the area of protein secondary structure prediction using artificial neural networks take a considerable time to get the most important knowledge in this area. This study was conducted to make the most foundational and directive knowledge in protein biological aspects, protein secondary structure prediction and protein neural network predictors get elucidated. Several neural network methods have contributed and influenced significantly the field of bioinformatics in general and the area of secondary structure prediction from protein sequences in specific. Present research suggest that there is a lot of work to be done to fully exploit artificial neural networks in this area.
  Nazar Zaki and Safaai Deris
  This study introduces a simple method based on representing protein sequence by fix dimensions of the length three. We present hidden Markov model combining scores method. Three scoring algorithms are combined to represent protein sequence of amino acids for better remote homology detection. We tested the method on the SCOP version 1.37 dataset. The results show that, with such a simple representation, we are able to achieve superior performance to previously presented protein homology detection methods while achieving better computational efficiency.
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