Genome-wide Association Studies (GWAS)

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https://www.sciencedirect.com/science/article/pii/S1018364722000283
Genome-wide Association Studies (GWAS) are conducted to identify single nucleotide polymorphisms (variants) associated with a phenotype within a specific population.

The primary purpose of machine learning algorithms is to define a function
which predicts an unknown phenotype based on a genotype observation based on sample data

A major reason for the adoption of machine learning algorithms is that they are well suited for developing predictive models when the number of features is larger than the number of samples.

A standard characteristic of GWAS results is that the number of attributes (p) greatly outnumber the number of sample points (n). This is usually described as the curse of dimensionality or the large p and small n problem.Ideally, it is a problem for classical multivariate regression.

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上面这篇文章 使用关键词 (Machine learning) AND (genome-wide association studies) 搜索后 还进行了人工过滤 过滤的条件包括：
1.不能是review articles类型的文章 https://www.sciencedirect.com/ 的文章本身就是分类的 这个可能在发表的时候就打上了标签

2.必须研究的是“人” 这个就涉及到语义搜索或者实体识别 能推断某个文章的研究对象包括人 把这个需求推广一下 科学家可能会搜索 某篇文章研究了某个基因？ 某篇文章研究了某个蛋白？某篇文章使用了ML方法？某个研究领域的文献？

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https://www.nature.com/articles/s41598-019-46649-z
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data