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fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

机译：fastJT：一个R包用于为机器学习和全基因组关联研究提供强大而有效的特征选择

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摘要

BackgroundParametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts necessitates highly efficient computational algorithms for the robust evaluation of many features.

机译：背景技术基于遗传数据的用于机器学习和关联研究的参数特征选择方法在离群值或有影响的观察方面并不可靠。尽管基于排名的无分布统计数据是参数方法的可靠替代方案，但它们的实际实用性受到限制，因为它们在分析高维数据时需要大量的计算资源。对于寻求鉴定变体的遗传研究，该假设受到了限制，因为通常假设基因型对表型的影响是单调的（例如加性遗传效应）。同样，用于机器学习应用程序的预测器可能具有自然的排序约束。在这些高维上下文中对特征选择进行交叉验证，需要高效的计算算法来对许多特征进行鲁棒评估。

著录项

期刊名称 BMC Bioinformatics
作者
Jiaxing Lin; Alexander Sibley; Ivo Shterev; Andrew Nixon; Federico Innocenti; Cliburn Chan; Kouros Owzar;
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作者单位

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年(卷),期 2019(20),-1
年度 2019
页码 333
总页数 11
原文格式 PDF
正文语种
中图分类应用微生物学;生化遗传学;生化药理学;
关键词
Jonckheere-Terpstra Linear rank statistic Constrained inference Robust statistic Logarithmic complexity Genome-wide association studies Feature selection Machine learning Parallel processing;

机译：Jonckheere-Terpstra;线性秩统计;约束推理;稳健统计;对数复杂度;全基因组关联研究;特征选择;机器学习;并行处理;

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专利

1. fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies [J] . Jiaxing Lin, Alexander Sibley, Ivo Shterev, BMC Bioinformatics . 2019,第1期

机译：FASTJT：用于机器学习和基因组协会研究的强大和高效特征选择的R包
2. Shrinkage estimation for robust and efficient screening of single-SNP association from case-control genome-wide association studies. [J] . Luo S, Mukherjee B, Chen Genetic epidemiology. . 2009,第8期

机译：收缩率估计，可从病例对照全基因组关联研究中可靠而有效地筛选单个SNP关联。
3. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection [J] . Chikersal Prerna, Doryab Afsaneh, Tumminia Michael, ACM Transactions on Computer-Human Interaction . 2021,第1期

机译：使用被动传感捕获的纵向症状检测抑郁症并预测其发病：一种具有鲁棒特征选择的机器学习方法
4. Feature Selection for Detecting Gene-Gene Interactions in Genome-Wide Association Studies [C] . Faramarz Dorani, Ting Hu European conference on applications of evolutionary computation . 2018

机译：在全基因组关联研究中检测基因-基因相互作用的特征选择
5. Assessment of Sparse Regression Machine Learning Methods for Genome-Wide Association Studies [D] . Yi, Hui. 2014

机译：对全基因组关联研究的稀疏回归机器学习方法的评估
6. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists [O] . Muhammad Attique Khan, Imran Ashraf, Majed Alhaisoni, 2020

机译：使用深度学习和强大的特征选择的多模式脑肿瘤分类：放射科学家的机器学习应用
7. Shrinkage estimation for robust and efficient screening of single-SNP association from case-control genome-wide association studies [O] . Luo, Sheng, Mukherjee, Bhramar, Chen, Jinbo, 2009

机译：收缩估计从病例对照全基因组关联研究中稳健有效地筛选单sNp关联

fastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

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