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Titlebook: Comparative Genomics; 20th International C Katharina Jahn,Tomá? Vina? Conference proceedings 2023 The Editor(s) (if applicable) and The Aut

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樓主: NERVE
21#
發(fā)表于 2025-3-25 05:56:46 | 只看該作者
22#
發(fā)表于 2025-3-25 10:56:32 | 只看該作者
,Finding Agreement Cherry-Reduced Subnetworks in?Level-1 Networks, direct relationship between finding an MACRS and calculating a distance based on cherry operations. As a result, the proposed algorithm also provides a distance that can be used for the comparison of level-1 networks..Supplementary material for this paper can be found on arXiv.org.
23#
發(fā)表于 2025-3-25 14:28:14 | 只看該作者
24#
發(fā)表于 2025-3-25 16:22:37 | 只看該作者
,MAGE: Strain Level Profiling of?Metagenome Samples,n maximum likelihood estimation. Unlike the existing approaches that often rely on strain-specific markers and homology information for deep profiling, MAGE works solely with read mapping information, which is the set of target strains from the reference collection for each mapped read. As part of M
25#
發(fā)表于 2025-3-25 21:46:43 | 只看該作者
26#
發(fā)表于 2025-3-26 00:57:44 | 只看該作者
Ein Vorgehensmodell für den Software-Entwurf DCJ distance. With this class of problems it is possible to investigate the complexity of the double distance under the . distance, increasing the value . in an attempt to identify the smallest value for which the double distance becomes NP-hard, indicating the point in which the complexity changes
27#
發(fā)表于 2025-3-26 07:25:58 | 只看該作者
28#
發(fā)表于 2025-3-26 08:44:11 | 只看該作者
29#
發(fā)表于 2025-3-26 15:17:07 | 只看該作者
Ein Vorgehensmodell für den Software-Entwurfting branch lengths and evolutionary model parameters. They also show that a flexible prior model could provide better results than a predefined prior model. Finally, the results highlight that using neural networks improves the initialization of the optimization of the prior density parameters.
30#
發(fā)表于 2025-3-26 17:02:00 | 只看該作者
Jochen Seemann,Jürgen Wolff Gudenbergy cost, a relaxation of the original Affinity cost to compare heterogeneous trees. We demonstrate that the characteristics of this cost are similar to the symmetric Cluster Affinity distance. Further, for the asymmetric affinity cost we describe efficient algorithms, derive the exact diameters, and
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