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Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 8th European Confere Clara Pizzuti,Marylyn D. Ritchie,Mario G

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樓主: metamorphose
51#
發(fā)表于 2025-3-30 11:49:31 | 只看該作者
52#
發(fā)表于 2025-3-30 16:01:10 | 只看該作者
A Local Search Appproach for Transmembrane Segment and Signal Peptide Discriminationiors of proteins with the aim of ensuring good prediction. The LS Prediction approach is assessed on a constructed data set from Swiss-Prot database and compared with one of the best methods from the literature.
53#
發(fā)表于 2025-3-30 19:31:27 | 只看該作者
54#
發(fā)表于 2025-3-30 21:14:27 | 只看該作者
55#
發(fā)表于 2025-3-31 01:00:24 | 只看該作者
António Teodoro,Manuela Guilhermeresults obtained are compared with the ones reached using the deletion of reactions, showing that we obtain solutions with similar quality levels and number of knockouts, but biologically more feasible. Indeed, we show that several of the previous solutions are not viable using the provided rules.
56#
發(fā)表于 2025-3-31 05:41:46 | 只看該作者
https://doi.org/10.1007/978-3-319-99265-5xperimental results show that the Rotation Forest enhanced the protein folding prediction accuracy better than the other applied Meta classifiers, as well as the previous works found in the literature.
57#
發(fā)表于 2025-3-31 11:51:17 | 只看該作者
58#
發(fā)表于 2025-3-31 16:51:32 | 只看該作者
59#
發(fā)表于 2025-3-31 20:54:07 | 只看該作者
Using Rotation Forest for Protein Fold Prediction Problem: An Empirical Studyxperimental results show that the Rotation Forest enhanced the protein folding prediction accuracy better than the other applied Meta classifiers, as well as the previous works found in the literature.
60#
發(fā)表于 2025-4-1 00:53:03 | 只看該作者
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