Deep Learning Annotation Models
Deep Learning Annotation Models is a computational annotation framework that leverages deep neural architectures to automatically infer and assign functional labels to biological sequences by learning hierarchical feature representations from large-scale genomic data.
207
Publications
15.3K
Citations
1.2K
Authors
497
Institutions
Publications per year
2017–2026
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National Institutes of Health Clinical Center, United States
3 publications · 672 citations
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497
Stanford University
Stanford, United States
9 h-index
30 publications
1.9K citations
Harvard University
Cambridge, United States
7 h-index
28 publications
6.2K citations

King Abdullah University of Science and Technology
Jeddah, Saudi Arabia
6 h-index
16 publications
2.2K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
14 publications
655 citations
Massachusetts General Hospital
Boston, United States
4 h-index
25 publications
3.6K citations
Tsinghua University
Beijing, China
4 h-index
17 publications
689 citations

Washington University in St. Louis
St Louis, United States
4 h-index
11 publications
1K citations
Technical University of Munich
Munich, Germany
4 h-index
13 publications
4K citations
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Repository · 13 publications · 358 citations
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Repository · 7 publications · 253 citations
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7 publications · 205 citations
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7 publications · 873 citations
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6 publications · 540 citations
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