Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

High-throughput genomic and proteomic technologies are widely used in cancer research to
build better predictive models of diagnosis, prognosis and therapy, to identify and …

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of
acquisition technique, quantification tool and processing method has emerged as the …

Individual variations in drug efficacy, side effects and adverse drug reactions are still
challenging that cannot be ignored in drug research and development. The aim of …

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 …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

For most bioinformatics statistical methods, particularly for gene expression data
classification, prognosis, and prediction, a complete dataset is required. The gene sample …

Recent advancements in signal processing and computerised methods are expected to
underpin the future progress of biomedical research and technology, particularly in …

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

Death, injury and disability resulting from road traffic crashes continue to be a major global
public health problem. Recent data suggest that the number of fatalities from traffic crashes …