Harnessing Protein Language Models to improve classic Local Pairwise Alignments 
for more sensitive and scalable Deep Homology Detection

Behr, C

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Harnessing Protein Language Models to improve classic Local Pairwise Alignments for more sensitive and scalable Deep Homology Detection

Behr, C. (2022). Harnessing Protein Language Models to improve classic Local Pairwise Alignments for more sensitive and scalable Deep Homology Detection (Master Thesis, Ebherhard-Karls-Universität, Tübingen, Germany, 2022).

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-974C-5 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-B7D7-3

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MA_2022_DIAMOND_Behr.pdf (Abstract), 207KB

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Creators:
Behr, C^{1, 2}, Author

Affiliations:
1Department Molecular Biology, Max Planck Institute for Biology Tübingen, Max Planck Society, ou_3371687
2Computational Biology Group, Department Molecular Biology, Max Planck Institute for Biology Tübingen, Max Planck Society, ou_3496867

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Dates: Published Online: 2022-12-19

Publication Status: Published online

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Publishing info: Tübingen, Germany : Ebherhard-Karls-Universität

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Degree: Master

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