Abstract Machine learning is one of the essential and effective tools in analyzing highly
complex medical data. With vast amounts of medical data being generated, there is an …

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are
often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases …

Microbiomes from every environment contain a myriad of uncultivated archaeal and
bacterial viruses, but studying these viruses is hampered by the lack of a universal, scalable …

Self-organizing maps (SOMs) are popular tools for grou** and visualizing data in many
areas of science. This paper describes recent changes in package kohonen, implementing …

Recent technological developments in high‐dimensional flow cytometry and mass cytometry
(CyTOF) have made it possible to detect expression levels of dozens of protein markers in …

Clustering is a powerful machine learning tool for detecting structures in datasets. In the
medical field, clustering has been proven to be a powerful tool for discovering patterns and …

Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the
transcriptome of individual cells. However, the data from scRNASeq experiments is often …

A critical step in the analysis of large genome-wide gene expression datasets is the use of
module detection methods to group genes into co-expression modules. Because of …

Synapses are found in vast numbers in the brain and contain complex proteomes. We
developed genetic labeling and imaging methods to examine synaptic proteins in individual …

The ability to quantify differentiation potential of single cells is a task of critical importance.
Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation …