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Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2006: E Franz Rothlauf,Jürgen Branke,Hideyuki Takagi Conference proceedings 2006 Sprin

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樓主: infection
11#
發(fā)表于 2025-3-23 11:39:38 | 只看該作者
Hierarchical Clustering, Languages and Canceree for the different cancers in the NCI60 microarray dataset (comprising gene expression data for 60 cancer cell lines). In this case, the method seems to support the current belief about the heterogeneous nature of the ovarian, breast and non-small-lung cancer, as opposed to the relative homogeneit
12#
發(fā)表于 2025-3-23 15:46:02 | 只看該作者
13#
發(fā)表于 2025-3-23 18:29:04 | 只看該作者
Comparison of Neural Network Optimization Approaches for Studies of Human Geneticsw method has high power to detect gene-gene interactions in simulated data. We also compare the performance of GENN to GPNN, a traditional back-propagation neural network (BPNN) and a random search algorithm. GENN outperforms both BPNN and the random search, and performs at least as well as GPNN. Th
14#
發(fā)表于 2025-3-23 22:17:00 | 只看該作者
Multi-Objective Evolutionary Algorithm for Discovering Peptide Binding Motifsatisfies two objectives: extract prior information by minimizing the distance between the experimentally derived motifs and the resulting matrix by MOEA; minimize the overall number of false positives and negatives resulting by using the putative MOEA-derived motif. The MOEA results in a Pareto opti
15#
發(fā)表于 2025-3-24 05:52:36 | 只看該作者
Mining Structural Databases: An Evolutionary Multi-Objetive Conceptual Clustering Methodologyubstructures. We apply EMO-CC to the Gene Ontology database to recover interesting substructures that describes problems from different points of view and use them to explain inmuno-inflammatory responses measured in terms of gene expression profiles derived from the analysis of longitudinal blood e
16#
發(fā)表于 2025-3-24 06:39:22 | 只看該作者
17#
發(fā)表于 2025-3-24 11:00:32 | 只看該作者
https://doi.org/10.1007/978-0-387-30424-3 their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization.
18#
發(fā)表于 2025-3-24 16:04:13 | 只看該作者
19#
發(fā)表于 2025-3-24 22:58:37 | 只看該作者
20#
發(fā)表于 2025-3-25 01:22:52 | 只看該作者
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