Single-cell RNA sequencing provides high-throughput gene expression information to
explore cellular heterogeneity at the individual cell level. A major challenge in characterizing …

With the rapid development of science and technology, high-dimensional data have been
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …

The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides
strong technical support for accurate and efficient analyzing single-cell gene expression …

Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it
possible to solve biological problems at the single-cell resolution. One of the critical steps in …

Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data
plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to …

In data sets some attributes may have higher or lower importance. One of the tools used for
data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy …

We developed a method for building gene expression profiles from single-cell gene
expression matrices. We named these profiles the “single-cell-derived-class” or SCDC …

Background Single cell sequencing is a technology for high-throughput sequencing analysis
of genome, transcriptome and epigenome at the single cell level. It can improve the …

Traditional clustering methods neglect the data quality and perform clustering directly on the
original data. Therefore, their performance can easily deteriorate since real‐world data …

Founded on the premise that high-dimensional data can be characterized as data drawn
from a union of several low-dimensional subspaces, subspace clustering has become …