We often describe cinema with subjective adjectives like "gritty," "vibrant," or "chaotic," but a film is fundamentally a high-dimensional data signal—millions of pixels shifting over time. This ...
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 ...
Single-cell RNA sequencing (scRNA-seq) enables detailed analysis of cellular diversity, but the data’s high dimensionality presents analytical challenges. We compare four dimensionality reduction ...
Are you frustrated with the linear assumptions of Principal Component Analysis (PCA) or losing the global structure and relationships in your data with t-SNE? Leveraging Riemannian geometry, Uniform ...
lmcinnes.umap (Run test suite run_platform_tests linux_py310) lmcinnes.umap (Run test suite run_platform_tests linux_py311) lmcinnes.umap (Run test suite run_platform ...
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation ...
To understand the importance of eIF4F components, we employed computational methods on large public datasets to investigate the impact of positive selection on eIF4F dysregulation in cancer. By ...
Sometimes, in the quest for new data, scientists forget that existing datasets can be treasure troves for new discoveries. One such undermined scientific gem is transcriptomic data from the human ...
Cuba legalized same-sex marriage in September. In addition to being an advance in rights, it could also be interpreted as an act of historical justice within a regime that locked up homosexuals in its ...
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