数量性状的遗传预测：一份聚焦身高的机器学习研究者指南
Genetic prediction of quantitative traits: a machine learner's guide focused on height
AI总结：
本指南面向机器学习研究者，聚焦身高这一连续表型，概述遗传复杂性状预测的前沿模型、基准数据集、混杂因素等关键要点，助力相关新模型开发。
摘要：
机器学习与深度学习在生物问题应用中已取得诸多成功，尤其在蛋白质折叠领域。另一个同等复杂且重要的问题却相对未受机器学习界关注，即基于遗传学预测复杂性状。解决该问题需要深入掌握相关遗传学文献知识，并了解遗传数据相关的各类细微问题。本指南为机器学习界概述了表型预测领域的当前最优模型，以及开发新的表型预测模型时需考量的相关细节。我们以身高作为连续值表型的示例，介绍了基准数据集、混杂因素、特征选择及常用评估指标。
英文摘要：
Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.
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。
