With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …

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

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate
quantitative protein abundance data generated using mass-spectrometry-based analysis …

Missing data is a common problem in real-world settings and for this reason has attracted
significant attention in the statistical literature. We propose a flexible framework based on …

One of the important trends in an intelligent data analysis will be the growing importance of
data processing. But this point faces problems similar to those of data mining (ie, high …

Rapid advances in high-throughput genomic technology have enabled biology to enter the
era of 'Big Data'(large datasets). The plant science community not only needs to build its …

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate
data with missing values, which may profoundly affect downstream analyses. A wide variety …

Introduction: The advent of high throughput technologies capable of comprehensive
analysis of genes, transcripts, proteins and other significant biological molecules has …

Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …

The performance of the classification algorithm depends on the quality of the training data.
Data quality is an important factor that affects the data mining classification results. However …