arXiv Machine Learning August 13, 2026 By Fenosoa Randrianjatovo, Maya Saleh, Simon Girard, Amadou Barry

CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data

Read the original on arXiv Machine Learning →

arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

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rag multimodal benchmarks safety

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Related stories

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

DMT-Dens: Density-preserving manifold visualization for biological data

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

LATTICE: Graph Self-Supervised Learning for Multimodal Spatial Omics Integration

Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting