首页|Enhancing Detection of Polygenic Adaptation: A Comparative Study of Machine Lear ning and Statistical Approaches Using Simulated Evolve-and-Resequence Data
Enhancing Detection of Polygenic Adaptation: A Comparative Study of Machine Lear ning and Statistical Approaches Using Simulated Evolve-and-Resequence Data
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Detecting signals of polygenic adaptation remains a significant challenge in ev olutionary biology, astraditional methods often struggle to identify the associ ated subtle, multi-locus allele-frequency shifts.Here, we introduced and tested several novel approaches combining machine learning techniques withtraditional statistical tests to detect polygenic adaptation patterns. We implemented a Nai ve BayesianClassifier (NBC) and One-Class Support Vector Machines (OCSVM), and compared their performanceagainst the Fisher\’s Exact Test (FET).
NETL
NSTL
