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 …

This paper presents a first-time review of the open literature focused on the significance of
big data generated within nano-sensors and nano-communication networks intended for the …

pcaMethods is a Bioconductor compliant library for computing principal component analysis
(PCA) on incomplete data sets. The results can be analyzed directly or used to estimate …

Many metabolomics, and other high-content or high-throughput, experiments are set up
such that the primary aim is the discovery of biomarker metabolites that can discriminate …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

Missing data is a common drawback in many real-life pattern classification scenarios. One of
the most popular solutions is missing data imputation by the K nearest neighbours (KNN) …

Abstract Treatment of missing data has become increasingly significant in scientific research
and engineering applications. The classic imputation strategy based on the K nearest …

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

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

Missing data is one of the most common issues encountered in data cleaning process
especially when dealing with medical dataset. A real collected dataset is prone to be …