Zobrazeno 1 - 10 of 243 015 pro vyhledávání: '"RNA-SEQ DATA"'
2
Akademický článek
3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data.
Autor: Tabaro, Francesco1 (AUTHOR) francescotabaro@embl.it, Boulard, Matthieu1 (AUTHOR)
Publikováno v: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-9. 9p.
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3
Akademický článek
scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data.
Autor: Kang, Yue1 (AUTHOR), Zhang, Hongyu1 (AUTHOR), Guan, Jinting1,2 (AUTHOR) jtguan@xmu.edu.cn
Publikováno v: Briefings in Bioinformatics. May2024, Vol. 25 Issue 3, p1-12. 12p.
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4
Akademický článek
CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data.
Autor: Liu, Haibo1 (AUTHOR) haibo.liu@umassmed.edu, Hu, Kai1 (AUTHOR) kai.hu@umassmed.edu, O'Connor, Kevin1 (AUTHOR) michelle.kelliher@umassmed.edu, Kelliher, Michelle A.1 (AUTHOR), Zhu, Lihua Julie1,2,3 (AUTHOR) julie.zhu@umassmed.edu
Publikováno v: BioTech. Sep2024, Vol. 13 Issue 3, p30. 21p.
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5
Report
Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data
Autor: Ma, Hui, Chen, Kai
Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE
Externí odkaz: http://arxiv.org/abs/2410.00473
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6
Report
scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data
Autor: Min, Wenwen, Wang, Zhen, Zhu, Fangfang, Xu, Taosheng, Wang, Shunfang
Single-cell RNA sequencing (scRNA-seq) data analysis is pivotal for understanding cellular heterogeneity. However, the high sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clustering metho
Externí odkaz: http://arxiv.org/abs/2408.05258
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7
Akademický článek
Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.
Autor: Park, Sejin1 (AUTHOR), Lee, Hyunju1,2 (AUTHOR) hyunjulee@gist.ac.kr
Publikováno v: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-14. 14p.
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8
Akademický článek
m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.
Autor: Yu, Boyi1 (AUTHOR), Nagae, Genta2 (AUTHOR), Midorikawa, Yutaka3 (AUTHOR), Tatsuno, Kenji2 (AUTHOR), Dasgupta, Bhaskar1 (AUTHOR), Aburatani, Hiroyuki2 (AUTHOR), Ueda, Hiroki1 (AUTHOR) ueda@biods.rcast.u-tokyo.ac.jp
Publikováno v: Briefings in Bioinformatics. Nov2024, Vol. 25 Issue 6, p1-13. 13p.
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9
Akademický článek
A comprehensive workflow for optimizing RNA-seq data analysis.
Autor: Jiang, Gao1 (AUTHOR), Zheng, Juan-Yu1 (AUTHOR), Ren, Shu-Ning2 (AUTHOR), Yin, Weilun2 (AUTHOR), Xia, Xinli2 (AUTHOR), Li, Yun1 (AUTHOR) liyun@bjfu.edu.cn, Wang, Hou-Ling2 (AUTHOR) whling@bjfu.edu.cn
Publikováno v: BMC Genomics. 6/24/2024, Vol. 25 Issue 1, p1-21. 21p.
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10
Akademický článek
A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.
Autor: Lüleci, Hatice Büşra1 (AUTHOR), Uzuner, Dilara1 (AUTHOR), Cesur, Müberra Fatma1 (AUTHOR), İlgün, Atılay1 (AUTHOR), Düz, Elif1 (AUTHOR), Abdik, Ecehan1 (AUTHOR), Odongo, Regan1 (AUTHOR), Çakır, Tunahan1 (AUTHOR) tcakir@gtu.edu.tr
Publikováno v: NPJ Systems Biology & Applications. 10/24/2024, Vol. 10 Issue 1, p1-12. 12p.
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