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Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification

• Authors : Jiang, Linfeng; Zhu, Yuan

Subjects: Quantitative Biology - Genomics; Computer Science - Information Theory; text

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FAVA:High-quality functional association networks inferred from scRNA-seq and proteomics data

• Authors : Koutrouli, Mikaela; Nastou, Katerina; Piera Líndez, Pau

Subjects: article

• Source: Koutrouli , M , Nastou , K , Piera Líndez , P , Bouwmeester , R , Rasmussen , S , Martens , L & Jensen , L J 2024 , ' FAVA : High-quality functional association networks inferred from

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scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation

• Authors : Li, Cong; Xiao, Meng; Wang, Pengfei

Subjects: Quantitative Biology - Genomics; Computer Science - Artificial Intelligence; text

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Binarized single cell RNA sequencing data clustering: The impact of binarized scRNA-seq data on clustering through community detection algorithms

• Authors : Theunisz, Jurriën (author)

Subjects: scRNA-seq; binary clustering; community detection

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SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types

• Authors : Ministerio de Ciencia, Innovación y Universidades (España); Agencia Estatal de Investigación (España); Instituto de Salud Carlos III

Subjects: Cell signaling; Cell-type identification; Deep learning

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Mostly natural sequencing-by-synthesis for scRNA-seq using Ultima sequencing

• Authors : Massachusetts Institute of Technology. Department of Biology; Simmons, Sean K; Lithwick-Yanai, Gila

Subjects: Article; http://purl.org/eprint/type/JournalArticle

• Source: Nature

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scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

• Authors : Jiang, Weikang; Wang, Jinxian; Guan, Jihong

Subjects: Quantitative Biology - Genomics; Computer Science - Artificial Intelligence; Computer Science - Machine Learning

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Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE

• Authors : Hozumi, Yuta; Wei, Gu-Wei

Subjects: Computer Science - Machine Learning; text

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Modelling and Quantification of scRNA-seq Experiments and the Transcriptome Dynamics of the Cell Cycle

• Authors : Falcke, Martin; Rajewsky, Nikolaus; Klipp, Edda

Subjects: Einzelzellsequenzierung; scRNA-Seq; mathematische Modellierung

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D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

• Authors : Jiang, Jialong; Chen, Sisi; Tsou, Tiffany

Subjects: Report or Paper; PeerReviewed

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