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Titlebook: Bioinformatics Research and Applications; 5th International Sy Ion M?ndoiu,Giri Narasimhan,Yanqing Zhang Conference proceedings 2009 Spring

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樓主: Philanthropist
41#
發(fā)表于 2025-3-28 16:48:22 | 只看該作者
42#
發(fā)表于 2025-3-28 19:13:22 | 只看該作者
Zusammenfassung und Schlussfolgerungen, sequencing, SELEX yields large sets of affine sequences from which binding affinity models such as positional weight matrices (PWMs) can be estimated. The talk will describe some recent developments in identifying various affinity models from SELEX data.
43#
發(fā)表于 2025-3-28 23:04:34 | 只看該作者
,Motif Construction from High–Throughput SELEX Data, sequencing, SELEX yields large sets of affine sequences from which binding affinity models such as positional weight matrices (PWMs) can be estimated. The talk will describe some recent developments in identifying various affinity models from SELEX data.
44#
發(fā)表于 2025-3-29 03:27:28 | 只看該作者
45#
發(fā)表于 2025-3-29 11:11:19 | 只看該作者
46#
發(fā)表于 2025-3-29 15:19:09 | 只看該作者
, und Kaufpreiszahlung in , und ,,rge evolutionary distance to the closest assembled outgroup, very few methods have been published with a reconstruction of the amniote ancestral genome. This one is the first which is founded on a simple and formal methodological framework, whose good stability is shown and whose CARs cover large regions of the human and chicken genomes.
47#
發(fā)表于 2025-3-29 18:58:28 | 只看該作者
Hierarchical Organization of Functional Modules in Weighted Protein Interaction Networks Using Clus by GO annotations. Many significant functional modules are detected, most of which are corresponding to the known complexes. Moreover, our algorithm HC-Wpin is faster and more accurate compared to other previous algorithms. The program is available at http://bioinfo.csu.edu.cn/limin/HC-Wpin.
48#
發(fā)表于 2025-3-29 19:54:55 | 只看該作者
49#
發(fā)表于 2025-3-30 01:39:39 | 只看該作者
50#
發(fā)表于 2025-3-30 04:49:29 | 只看該作者
Mean Square Residue Biclustering with Missing Data and Row Inversions,ntroduced by Cheng and Church in [4]. Since, missing data often occur in the given data matrices for biclustering, usually, missing data are filled by random numbers. However, we show that ignoring the missing data is a better approach and avoids additional noise caused by randomness. Since, an idea
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