Recent advances in sequencing, mass spectrometry and cytometry technologies have
enabled researchers to collect large-scale omics data from the same set of biological …

The heterogeneous and high-dimensional nature of omics data presents various challenges
in gaining insights while analysis. In the era of big data, omics data is available as genome …

MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing
and post-transcriptional regulation of gene expression. An increasing number of studies …

Predictive modeling from high-dimensional genomic data is often preceded by a dimension
reduction step, such as principal component analysis (PCA). However, the application of …

MicroRNAs (miRNAs) are small endogenous regulatory molecules that modulate gene
expression post-transcriptionally. Although differential expression of miRNAs have been …

Nowadays, the heterogeneous characteristics of cancer patients throw a big challenge to
precision medicine and targeted therapy. Identifying cancer subtypes shed new light on …

Objective There has been exponential growth in the volume, variety, and velocity of cancer
data. Cloud computing (CC) plays a pivotal role in the managing of cancer big data by …

The immunological immaturity of the innate immune system during the first-week post-hatch
enables pathogens to infect chickens, leading to the death of the animals. Current …

The general disease group of malignant neoplasms depicts one of the leading and
increasing causes for death. The underlying complexity of cancer demands for abstractions …

We consider binary classification in the high-dimensional setting, where the number of
features is huge, and the number of observations is limited. We focus on the setting where …