Machine learning, the kidney, and genotype-phenotype analysis

被引:27
作者
Sealfon, Rachel S. G. [1 ]
Mariani, Laura H. [2 ]
Kretzler, Matthias [2 ]
Troyanskaya, Olga G. [1 ,3 ,4 ]
机构
[1] Simons Fdn, Flatiron Inst, Ctr Computat Biol, New York, NY USA
[2] Univ Michigan, Div Nephrol, 1560 MSRB II,1150 W Med Ctr Dr,SPC5676, Ann Arbor, MI 48109 USA
[3] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 USA
[4] Princeton Univ, Dept Comp Sci, Comp Sci Bldg,Room 320,35 Olden St, Princeton, NJ 08544 USA
基金
美国国家卫生研究院;
关键词
deep learning; genotype; machine learning; NONCODING VARIANTS; APOL1; GENE; DISEASE; PREDICTION; NEPHROPATHY; MODELS; PATHOGENICITY; ASSOCIATION; PROGRESSION; NETWORK;
D O I
10.1016/j.kint.2020.02.028
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.
引用
收藏
页码:1141 / 1149
页数:9
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