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Interpretable representation learning for 3D multi-piece intracellular structures using point clouds

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.07.25.605164Source: DataRank Database

Interpretable representation learning for 3D multi-piece intracellular structures using point clouds is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2024). On theSindex it has a DataRank of 0.

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Authors (11)

Alexandra J. Ferrante,Antoine Borensztejn ORCID,Christopher L. Frick ORCID,Nathalie Gaudreault ORCID,Saurabh S. Mogre ORCID

Live enrichment

PubMed:39091871 OpenAlex:https://openalex.org/W4401022607

National Institutes of Health

Grant: 5UM1HG011593-03

Multiscale Analyses of 4D Nucleome Structure and Function by Comprehensive Multimodal Data Integration

NHGRI NIH HHS

Grant: UM1 HG011593

greenLicense: cc-by

repositoryhttps://doi.org/10.1101/2024.07.25.605164

GREENhttps://doi.org/10.1101/2024.07.25.605164

repositoryhttps://doi.org/10.1101/2024.07.25.605164

publisherhttps://syndication.highwire.org/content/doi/10.1101/2024.07.25.605164

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39091871

Fields of Study

Cell Image Analysis TechniquesGenomics and Chromatin DynamicsSingle-cell and spatial transcriptomicsBiologyMedicineComputer Science0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceFeature learningPython (programming language)Artificial intelligencePoint cloudBenchmarkingRepresentation (politics)Deep learningPattern recognition (psychology)Machine learningArticle

Sustainable Development Goals

SDG 0: Partnerships for the goals