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Protein secondary structure prediction from its amino acid sequence is a well studied computational problem in bioinformatics and data mining. It can be viewed as an intermediate research objective to solving the more challenging protein three-dimensional structure prediction problem, which is one of the most important research goals of bioinformatics. Although the secondary structure prediction problem...
Prediction of a protein's secondary structure from its amino acid sequence is a well studied computational problem in bioinformatics, and has significant practical research value. Although the secondary structure prediction problem was first defined almost fifty years ago, the accuracy of most modern methods still hovers around 80%. In [1] this research team presented a promising protein secondary...
Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. 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. In this paper, the...
This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, hybrid windows prediction (HWP) and similar searching prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction...
In this paper, the algorithm of a kind of hierarchical competitive covering networks for the classification problems is proposed based on the quotient space theory, which defines granularity according to Huffman coding. Not only three classes of secondary structure but also eight classes are discussed for the protein. Instances show that this kind of networks improves the sorting ability of covering...
As one of KDTICM theory researches, this paper propose an improved algorithm - CBA, which is based on KDD* model and combined with KAAPRO method, for protein secondary structure prediction problem. Further, multilayer systematic prediction model--compound pyramid model, is proposed. The kernel of this model is CBA which is a classic association rules analysis algorithm. Domain knowledge is used through...
At first, this paper reviews the development history of the protein secondary structure prediction. Some concerned secondary structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49% and RS126 with 82.79% respectively. In the end, this paper points out several possible trends in the protein secondary structure...
Protein secondary structure prediction is a bridge between amino acid sequence and tertiary prediction. Various methods have been used to improve the prediction accuracy and have been developed greatly. Protein classification is a multi-class classification problem. For traditional method, the three structure are predicted in the same time. But it can be degraded to a set of binary classification...
With the increase of data from genome sequencing projects comes the need for reliable and efficient methods for the analysis and classification of protein motifs and domains. Experimental methods currently used to determine protein structure are accurate, yet expensive both in terms of time and equipment. Therefore, various computational approaches to solving the problem have been attempted, although...
This paper presents a grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (support vector machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential...
Based on neural network, an improvement scheme that iterative matrix replace secondary derivative has been developed by introduced quasi-Newton algorithm. Profile code based on probability has been used and comparison of window width and learning training has been completed. The experiment results indicate that the prediction for secondary structures of protein obtain a very good effect based on neural...
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