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Johannes Linder

Showing results (1-10 of 19) with videos related to

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BMC Bioinformatics|October 21, 2021
Fast activation maximization for molecular sequence designJohannes Linder, Georg Seelig
Nature Methods|May 25, 2026
Teaching an old dog new cellsJohannes Linder, David R Kelley
Biorxiv : the Preprint Server for Biology|March 3, 2025
Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read CoverageIan Holmes, Johannes Linder, David Kelley
Genome Biology|January 31, 2026
Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contextsHan Yuan, Johannes Linder, David R Kelley
Genome Biology|November 6, 2022
Deciphering the impact of genetic variation on human polyadenylation using APARENT2Johannes Linder, Samantha E Koplik, Anshul Kundaje, et al.
Cell|June 11, 2019
A Deep Neural Network for Predicting and Engineering Alternative PolyadenylationNicholas Bogard, Johannes Linder, Alexander B Rosenberg, et al.
Cell Systems|July 27, 2020
A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein SequencesJohannes Linder, Nicholas Bogard, Alexander B Rosenberg, et al.
Nature Genetics|January 8, 2025
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulationJohannes Linder, Divyanshi Srivastava, Han Yuan, et al.
Nature Machine Intelligence|August 15, 2022
Interpreting Neural Networks for Biological Sequences by Learning Stochastic MasksJohannes Linder, Alyssa La Fleur, Zibo Chen, et al.
Research Square|November 24, 2025
Predicting dynamic expression patterns in budding yeast with a fungal DNA language modelKuan-Hao Chao, Majed Mohamed Magzoub, Emily Stoops, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
BMC Bioinformatics|October 21, 2021
Fast activation maximization for molecular sequence designJohannes Linder, Georg Seelig
Nature Methods|May 25, 2026
Teaching an old dog new cellsJohannes Linder, David R Kelley
Biorxiv : the Preprint Server for Biology|March 3, 2025
Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read CoverageIan Holmes, Johannes Linder, David Kelley
Genome Biology|January 31, 2026
Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contextsHan Yuan, Johannes Linder, David R Kelley
Genome Biology|November 6, 2022
Deciphering the impact of genetic variation on human polyadenylation using APARENT2Johannes Linder, Samantha E Koplik, Anshul Kundaje, et al.
Cell|June 11, 2019
A Deep Neural Network for Predicting and Engineering Alternative PolyadenylationNicholas Bogard, Johannes Linder, Alexander B Rosenberg, et al.
Cell Systems|July 27, 2020
A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein SequencesJohannes Linder, Nicholas Bogard, Alexander B Rosenberg, et al.
Nature Genetics|January 8, 2025
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulationJohannes Linder, Divyanshi Srivastava, Han Yuan, et al.
Nature Machine Intelligence|August 15, 2022
Interpreting Neural Networks for Biological Sequences by Learning Stochastic MasksJohannes Linder, Alyssa La Fleur, Zibo Chen, et al.
Research Square|November 24, 2025
Predicting dynamic expression patterns in budding yeast with a fungal DNA language modelKuan-Hao Chao, Majed Mohamed Magzoub, Emily Stoops, et al.
Pageof 2