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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 7th International Me Riccardo Rizzo,Paulo J. G. Lisboa Conference

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https://doi.org/10.1007/978-3-642-91337-2ather by adaptively compressing the sequence into a hidden feature vector. We benchmark SCL_pred against other publicly available predictors using two benchmarks including a new subset of Swiss-Prot release 57. We show that SCL_pred compares favourably to the other state-of-the-art predictors. Moreo
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https://doi.org/10.1007/978-3-322-81283-4different methods it appears that GRHCRFs perform slightly better than the others achieving a per protein accuracy of 87% with a Matthews correlation coefficient (C) of 0.73. Finally, we investigate the difference between disulfide bonding state predictions in Eukaryotes and Prokaryotes. Our analysi
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J. Rinklebe,K. Heinrich,H.-U. Neueits to find find several biclusters by using the statistical method of Bootstrap aggregation. We applied the algorithm to a synthetic data and to the Yeast dataset, obtaining fast convergence and good quality solutions. A comparison with original PBC method is also presented.
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