Open AccessMissing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations
Author(s) -
Zhuxuan Jin,
Jian Kang,
Tianwei Yu
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx816
Subject(s) - imputation (statistics) , missing data , computer science , data mining , machine learning
Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal.
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