Unsupervised Machine Learning Methods for Cell-Type Identification Unsupervised machine learning methods identify groups of cells that are similar to each other based on cytometry data itself without ...

If flow cytometry seems to discharge mere flotsam and jetsam, FlowSOM and other algorithms can salvage meaning, says Cytobank, a software-as-service company that helps analyze single-cell data.

In the present study, by validating unsupervised analysis of flow cytometry data with a semi-automated gating strategy and including proper control populations, we identify the peripheral cellular ...

RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically ...

A broad and practical overview for applying computational techniques to analyse, visualize and interpret high-dimensional data from flow cytometry. Computational flow cytometry improves ...

At the same time, the data analysis suite increases the accuracy, reproducibility, and quality of flow cytometry data. CytoML also makes it possible to leverage machine learning to scale-up and ...

Life science and data technology innovator, Aigenpulse, is launching its CytoML Experiment Suite – an automated, end-to-end, machine learning solution specifically aimed at streamlining and automating ...

Spectral flow cytometry is gaining attention in human immune monitoring as it generates data that compares well to mass cytometry (Ferrer-Font et al., 2020; Mistry et al., 2019). Spectral flow ...