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Keeping SCORE enables interpretable uncertainty-aware classification from diffusion models for genomics

bioRxiv (Cold Spring Harbor Laboratory)(2025)10.1101/2025.11.26.690838Source: DataRank Database

Keeping SCORE enables interpretable uncertainty-aware classification from diffusion models for genomics is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2025). On theSindex it has a DataRank of 0.

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Authors (8)

Jae-Kwon Jung,Pornchanan Pholraksa,Adrianne Zhong ORCID,Leon Schwartz ORCID,Ekta Prashnani ORCID

Live enrichment

PubMed:41377511 OpenAlex:https://openalex.org/W4416881210

NIGMS NIH HHS

Grant: R35 GM147400

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2025/12/01/2025.11.26.690838.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2025.11.26.690838

repositoryhttps://doi.org/10.1101/2025.11.26.690838

repositoryhttps://pubmed.ncbi.nlm.nih.gov/41377511

Fields of Study

Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGenerative Adversarial Networks and Image Synthesis

Keywords

InterpretabilityProbabilistic logicFeature (linguistics)Class (philosophy)RegressionPattern recognition (psychology)Feature selectionUnivariateArtificial intelligenceBayesian inferenceDiffusion modelsDissipationThermodynamic classification