Sequence-based Distance Prediction
Sequence-Based Distance Prediction is a computational method in structural bioinformatics that predicts pairwise residue-residue distances in a protein structure exclusively from its amino acid sequence, distinguishing itself from structure-based or coevolutionary methods by relying solely on sequence-derived features and machine learning models.
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3.6K
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201
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96
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2017–2026
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De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku et al. · Nature · 2021 · 604 citations · Full text
Distance-based Support Vector Machine to Predict DNA N6-methyladenine Modification
Haoyu Zhang, Quan Zou, Ying Ju et al. · Current Bioinformatics · 2022 · 316 citations
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Chinese Academy of Sciences
Beijing, China
5 h-index
13 publications
690 citations
Institute of Computing Technology
Beijing, China
4 h-index
9 publications
496 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
11 publications
595 citations
Technical University of Denmark
Kongens Lyngby, Denmark
2 h-index
7 publications
96 citations

Howard Hughes Medical Institute
Chevy Chase, United States
2 h-index
2 publications
692 citations

Harvard University Press
Cambridge, United States
2 h-index
2 publications
692 citations
Institute of Theoretical Physics
Beijing, China
2 h-index
2 publications
99 citations
University of Missouri
Columbia, United States
2 h-index
6 publications
290 citations
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9 publications · 520 citations
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5 publications · 209 citations
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Repository · 4 publications · 143 citations
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PEDS · 3 publications · 600 citations
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Repository · 2 publications · 51 citations
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