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Titlebook: Bioinformatics Research and Applications; Third International Ion M?ndoiu,Alexander Zelikovsky Conference proceedings 2007 Springer-Verlag

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61#
發(fā)表于 2025-4-1 04:52:15 | 只看該作者
Discovering Relations Among GO-Annotated Clusters by Graph Kernel Methods, to perform clustering and then compute a functional characterization via enrichments by Gene Ontology terms [1]. To better assist the interpretation of results, it may be useful to establish connections among different clusters. This machine learning step is sometimes termed ., and several approach
62#
發(fā)表于 2025-4-1 09:42:33 | 只看該作者
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發(fā)表于 2025-4-1 12:52:00 | 只看該作者
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發(fā)表于 2025-4-1 18:03:51 | 只看該作者
An Adaptive Resolution Tree Visualization of Large Influenza Virus Sequence Datasets, be represented in an easy-to-comprehend form and allow convenient manipulation of the data..We developed an adaptive approach to visualization of large sequence datasets on the web. A dataset is presented in an aggregated tree form with special representation of sub-scale details. The representatio
65#
發(fā)表于 2025-4-1 20:21:21 | 只看該作者
Wavelet Image Interpolation (WII): A Wavelet-Based Approach to Enhancement of Digital Mammography Int of the microcalcifications, as well as morphological features of individual microcalcifications. We have developed state-of-the-art wavelet-based methods to enhance the resolution of microcalcifications visible on digital mammograms, aimed at improving the specificity of breast cancer diagnoses.
66#
發(fā)表于 2025-4-2 02:35:44 | 只看該作者
67#
發(fā)表于 2025-4-2 04:52:59 | 只看該作者
https://doi.org/10.1007/978-3-642-94197-9ich also plays an important role in the discovery of responsive genes while variance alone is not appropriate because of nonuniform noise variance across genes. Hence, we propose a novel . which effectively combines smoothness and variance of gene expression time-course. We demonstrate that dSEVRaT
68#
發(fā)表于 2025-4-2 09:15:11 | 只看該作者
69#
發(fā)表于 2025-4-2 14:42:43 | 只看該作者
,Der ged?mpfte harmonische Oszillator,n (BIC). This framework can incorporate most clustering algorithms and improve their performance. In this study we illustrate the effectiveness of this platform by incorporating the standard K-Means and the Quantum Clustering algorithms. The implementations are applied to several gene-expression ben
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