分散NN2:一个神经网络,用于从地理引用的多态数据中估计分散距离
Chris C R Smith1, Andrew D Kern1
1Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.
bioRxiv : the preprint server for biology
|August 14, 2023
概括
一个新的深度学习工具,disperseNN2,利用遗传数据估计了生物的分散距离. 它比现有的方法提高了准确性,特别是对于具有有限基因组资源的非模型物种.
科学领域:
- 人口遗传学 人口遗传学
- 计算生物学是一种计算生物学.
- 机器学习 机器学习
背景情况:
- 分散是塑造空间遗传变异的一个关键因素.
- 准确估计分散距离对于了解种群结构和演变至关重要.
- 现有的方法可能与非模型生物或有限的基因组数据扎.
结论:
- 分散NN2提供了一种强大且易于使用的方法,用于估计各种生物的分散.
- 该工具增强了我们研究分散的遗传后果的能力.
- 开源的可用性有助于在生态和进化研究中更广泛地采用.
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