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Robust self-supervised machine learning for single cell embeddings and annotations

bioRxiv (Cold Spring Harbor Laboratory)(2025)10.1101/2025.06.05.658097Source: DataRank Database

Robust self-supervised machine learning for single cell embeddings and annotations is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2025). On theSindex it has a DataRank of 0.

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Repositories this paper deposited data in (declared in PubMed).

Dryad10.5061/dryad.8t8s248

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (4 grants)OA: greenTopics (18)IDs (PubMed, OpenAlex)

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Open Access

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Authors (4)

Min Sun ORCID,Livnat Jerby‐Arnon ORCID,Livnat Jerby-Arnon ORCID,Christine Y. Yeh ORCID

Live enrichment

PubMed:40502088 OpenAlex:https://openalex.org/W4411132359

NLM NIH HHS

Grant: T15LM007033

NHGRI NIH HHS

Grant: U01HG012069

Ovarian Cancer Research Alliance

Grant: 889076

Grant: 1019508.01

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2025/06/08/2025.06.05.658097.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2025.06.05.658097

repositoryhttps://doi.org/10.1101/2025.06.05.658097

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40502088

Fields of Study

Single-cell and spatial transcriptomicsGene expression and cancer classificationCell Image Analysis Techniques

Keywords

OverfittingComputer scienceDimensionality reductionCluster analysisArtificial intelligenceMachine learningRobustness (evolution)Curse of dimensionalityNonlinear dimensionality reductionNoise (video)Data miningSystems biologyBioinformaticsBiologyImage (mathematics)