Reaching optimized parameter set: protein secondary structure prediction using neural network
نویسندگان
چکیده
منابع مشابه
Using Artificial Neural Network for Protein Secondary Structure prediction
Some properties of a protein can be determined from Knowing the secondary structure of the protein. The known structure of proteins so far was done using a technique called X-ray diffraction patterns of crystallized then the data from the process is fed through the DSSP algorithm(Kabsch and Sander, 1983) to determine the exact protein structure. The process is time consuming and expensive. Ther...
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Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (ML...
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Proteins are key biological molecules with diverse functions. With newer technologies producing more data (genomics, proteomics) than can be annotated manually, in silico methods of predicting their structure and thereafter their function has been christened the Holy Grail of structural bioinformatics. Successful secondary structure prediction provides a starting point for direct tertiary struc...
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Protein secondary structure prediction is a problem related to structural bioinformatics which deals with the prediction and analysis of macromolecules i.e. DNA, RNA and protein. It is an important step towards elucidating its three dimensional structure, as well as its function. Secondary structure of a protein can be predicted from its primary structures i.e. from the amino
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ژورنال
عنوان ژورنال: Neural Computing and Applications
سال: 2016
ISSN: 0941-0643,1433-3058
DOI: 10.1007/s00521-015-2150-2