technologynetworks

Single-Cell RNA-Seq Data Analysis With AI: Tools, Workflows, and Biological Interpretation

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...
eLife

TopoMetry systematically learns and evaluates the latent geometry of single-cell data

A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...
GitHub

demixed Principal Component Analysis (dPCA)

dPCA is a linear dimensionality reduction technique that automatically discovers and highlights the essential features of complex population activities. The population activity is decomposed into a ...
IEEE

CBMAP: Clustering-Based Manifold Approximation and Projection for Dimensionality Reduction

Abstract: Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional ...
ahajournals.org

Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree‐Based Dimensionality Reduction

Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle‐branch block (LBBB) and right bundle‐branch block or nonspecific ...
GitHub

pyforce: PYthon Framework for data-driven model Order Reduction of multi-physiCs problems

pyforce is a Python package implementing Data-Driven Reduced Order Modelling (DDROM) techniques for applications to multi-physics problems, mainly set in the Nuclear Engineering world. The package is ...