Linking Phenotypes and Genotypes with Matrix Factorizations
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作者:
Li, Jianqiang
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Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Beijing Engn Res Ctr IoT Software & Syst, Beijing 100124, Peoples R ChinaBeijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Li, Jianqiang
[1
,2
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Guan, Yu
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Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaBeijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Guan, Yu
[1
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Xu, Xi
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Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaBeijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Xu, Xi
[1
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Ma, Zerui
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Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R ChinaBeijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Ma, Zerui
[1
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Pei, Yan
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Univ Aizu, Sch Comp Sci & Engn, Aizu Wakamatsu 9658580, JapanBeijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
Pei, Yan
[3
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机构:
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Beijing Engn Res Ctr IoT Software & Syst, Beijing 100124, Peoples R China
Aims We linked phenotypes and genotypes by PheGe-Net, a unified operation frame. Background Genotype refers to the general name of all gene combinations of an individual. It reflects the genetic composition of organisms. Phenotype refers to the macroscopic characteristics of an organism that can be observed. Objective Identifying the phenotype-genotype association assists in the explanation of the pathogenesis and the progress of genomic medicine. Methods PheGe-Net exploited the similarity net of phenotypes and genotypes and recognized phenotype-genotype relationships to discover their hidden interactions. Results By conducting experiments with a real-world dataset, the validity of our PheGe-Net is verified. Our method outperformed the second-best one by around 3% on Accuracy and NMI when clustering the phenotype/genotype; it also successfully detected phenotype-genotype associations, for example, the association for obesity (OMIM ID: 601665) was analyzed, and among the top ten scored genes, two known ones were assigned with scores more than 0.75, and other eight predicted ones are also explainable. Conclusion PheGe-Net is not only able to discover latent phenotype or genotype clusters but also can uncover the hidden relationships among them, as long as there are known similarity networks of phenotype, genotype, and acknowledged pheno-genotype relationships.