LinkedNN: 用于近期有效种群规模推断的连锁不平衡衰减神经模型
LinkedNN: a neural model of linkage disequilibrium decay for recent effective population size inference
AI总结：
提出一种神经网络模型，通过自动计算多态性位点间基因组距离相关的连锁不平衡特征，来估计近期有效种群规模，在少量测序个体和变异位点下优于现有方法。
摘要：
摘要：介绍了一种生物信息学工具，通过使用神经网络自动计算多态性位点间基因组距离相关的连锁不平衡特征，来估计近期有效种群规模。新方法在相对较少的测序个体和变异位点下优于现有的深度学习和基于汇总统计的方法，使其特别适用于稀疏、无相位数据的分子生态学应用。可用性和实现：该程序可作为易于安装的Python包提供，文档见：this https URL。开源代码可从：this https URL获取。
英文摘要：
Summary: A bioinformatics tool is presented for estimating recent effective population size by using a neural network to automatically compute linkage disequilibrium-related features as a function of genomic distance between polymorphisms. The new method outperforms existing deep learning and summary statistic-based approaches using relatively few sequenced individuals and variant sites, making it particularly valuable for molecular ecology applications with sparse, unphased data. Availability and implementation: The program is available as an easily installable Python package with documentation here: https://pypi.org/project/linkedNN/. The open source code is available from: https://github.com/the-smith-lab/LinkedNN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
