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Yuk Kei Wan

Showing results (1-10 of 15) with videos related to

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Trends in Genetics : TIG|October 29, 2021
Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal dataYuk Kei Wan, Christopher Hendra, Ploy N Pratanwanich, et al.
Methods in Molecular Biology (Clifton, N.J.)|October 30, 2020
High-Throughput Protein Engineering by Massively Parallel Combinatorial MutagenesisYuk Kei Wan, Gigi C G Choi, Alan S L Wong
Bioinformatics (Oxford, England)|March 14, 2025
Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysisHiruna Samarakoon, Yuk Kei Wan, Sri Parameswaran, et al.
Nature Methods|November 10, 2022
Detection of m6A from direct RNA sequencing using a multiple instance learning frameworkChristopher Hendra, Ploy N Pratanwanich, Yuk Kei Wan, et al.
STAR Protocols|January 25, 2021
Extensible combinatorial CRISPR screening in mammalian cellsPeng Zhou, Yuk Kei Wan, Becky K C Chan, et al.
Nature Methods|June 12, 2023
Context-aware transcript quantification from long-read RNA-seq data with BambuYing Chen, Andre Sim, Yuk Kei Wan, et al.
Cell Systems|November 28, 2023
Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning modelsWeizhong Zheng, John H C Fong, Yuk Kei Wan, et al.
Nature Biotechnology|July 20, 2021
Identification of differential RNA modifications from nanopore direct RNA sequencing with xPorePloy N Pratanwanich, Fei Yao, Ying Chen, et al.
Communications Chemistry|April 12, 2025
The RMaP challenge of predicting RNA modifications by nanopore sequencingJannes Spangenberg, Stefan Mündnich, Anne Busch, et al.
RNA (New York, N.Y.)|October 10, 2023
Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq dataSam Bryce-Smith, Dominik Burri, Matthew R Gazzara, et al.
Pageof 2

Showing results (1-10 of 15) with videos related to

Sort By:
Pageof 2
Trends in Genetics : TIG|October 29, 2021
Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal dataYuk Kei Wan, Christopher Hendra, Ploy N Pratanwanich, et al.
Methods in Molecular Biology (Clifton, N.J.)|October 30, 2020
High-Throughput Protein Engineering by Massively Parallel Combinatorial MutagenesisYuk Kei Wan, Gigi C G Choi, Alan S L Wong
Bioinformatics (Oxford, England)|March 14, 2025
Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysisHiruna Samarakoon, Yuk Kei Wan, Sri Parameswaran, et al.
Nature Methods|November 10, 2022
Detection of m6A from direct RNA sequencing using a multiple instance learning frameworkChristopher Hendra, Ploy N Pratanwanich, Yuk Kei Wan, et al.
STAR Protocols|January 25, 2021
Extensible combinatorial CRISPR screening in mammalian cellsPeng Zhou, Yuk Kei Wan, Becky K C Chan, et al.
Nature Methods|June 12, 2023
Context-aware transcript quantification from long-read RNA-seq data with BambuYing Chen, Andre Sim, Yuk Kei Wan, et al.
Cell Systems|November 28, 2023
Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning modelsWeizhong Zheng, John H C Fong, Yuk Kei Wan, et al.
Nature Biotechnology|July 20, 2021
Identification of differential RNA modifications from nanopore direct RNA sequencing with xPorePloy N Pratanwanich, Fei Yao, Ying Chen, et al.
Communications Chemistry|April 12, 2025
The RMaP challenge of predicting RNA modifications by nanopore sequencingJannes Spangenberg, Stefan Mündnich, Anne Busch, et al.
RNA (New York, N.Y.)|October 10, 2023
Extensible benchmarking of methods that identify and quantify polyadenylation sites from RNA-seq dataSam Bryce-Smith, Dominik Burri, Matthew R Gazzara, et al.
Pageof 2