Kernel weighted least square approach for imputing missing values of metabolomics data.

ABSTRACT: Mass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and ou

SUBMITTER: Kumar N 

PROVIDER: S-EPMC8159923 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature