Bayesian network multi-classifiers for protein secondary structure prediction
نویسندگان
چکیده
منابع مشابه
Bayesian network multi-classifiers for protein secondary structure prediction
Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of prot...
متن کاملCascaded multiple classifiers for secondary structure prediction.
We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This...
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We introduce the family of multi-dimensional Bayesian network classifiers. These classifiers include one or more class variables and multiple feature variables, which need not be modelled as being dependent on every class variable. Our family of multi-dimensional classifiers includes as special cases the well-known naive Bayesian and tree-augmented classifiers, yet offers better modelling capab...
متن کاملProtein secondary structure prediction using distance based classifiers
De novo structure determination of proteins is a significant research issue of bioinformatics. Biochemical procedures for protein structure determination are costly. Use of different pattern classification techniques are proved to ease this task. In this article, the secondary structure prediction task has been mapped into a three-class problem of pattern classification, where the classes are h...
متن کاملBayesian Model of Protein Primary Sequence for Secondary Structure Prediction
Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure ...
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ژورنال
عنوان ژورنال: Artificial Intelligence in Medicine
سال: 2004
ISSN: 0933-3657
DOI: 10.1016/s0933-3657(04)00032-6