Recent advances in single-cell technologies have enabled high-throughput molecular
profiling of cells across modalities and locations. Single-cell transcriptomics data can now …

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Map** single-cell sequencing profiles to comprehensive reference datasets provides a
powerful alternative to unsupervised analysis. However, most reference datasets are …

The simultaneous measurement of multiple modalities represents an exciting frontier for
single-cell genomics and necessitates computational methods that can define cellular states …

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …

In this article, a challenging fault diagnosis task is studied, in which no samples of the target
faults are available for the model training. This scenario has hardly been studied in industrial …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Dimensionality reduction is one of the preprocessing steps in many machine learning
applications and it is used to transform the features into a lower dimension space. Principal …

Technologies that profile chromatin modifications at single-cell resolution offer enormous
promise for functional genomic characterization, but the sparsity of the measurements and …

A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …