Microarray gene expression data generally suffers from missing value problem due to a
variety of experimental reasons. Since the missing data points can adversely affect …

High-throughput biotechnologies, such as gene expression microarrays or mass-
spectrometry-based proteomic assays, suffer from frequent missing values due to various …

Introduction The generic metabolomics data processing workflow is constructed with a serial
set of processes including peak picking, quality assurance, normalisation, missing value …

OBJECTIVES: Missing data imputation is an important task in cases where it is crucial to use
all available data and not discard records with missing values. This work evaluates the …

Missing values in mass spectrometry metabolomic datasets occur widely and can originate
from a number of sources, including for both technical and biological reasons. Currently …

Missing data raise problems in almost all fields of quantitative research. A useful
nonparametric procedure is the nearest neighbor imputation method. Improved versions of …

Background The analysis of large-scale gene expression data is a fundamental approach to
functional genomics and the identification of potential drug targets. Results derived from …

Background Gene expression data frequently contain missing values, however, most down-
stream analyses for microarray experiments require complete data. In the literature many …

We present a modification of the weighted K-nearest neighbours imputation method
(KNNimpute) for missing values (MVs) estimation in microarray data based on the reuse of …

Most quantitative studies in the social sciences suffer from missing data. However, despite
the large availability of documents and software to treat such data, it appears that many …