Machine Learning-based Annotation
Machine Learning-Based Annotation is a computational approach to protein function prediction that employs machine learning models to infer functional labels from biological data, distinguishing itself from rule-based or homology-based methods by leveraging patterns learned from annotated training examples.
127
Publications
9.7K
Citations
607
Authors
263
Institutions
Publications per year
2017–2026
56
Parents
127
Bioinformatics: The Machine Learning Approach
J.A.M. Leunissen · Briefings in Bioinformatics · 2002 · 478 citations · Full text
MGnify: the microbiome sequence data analysis resource in 2023
Lorna Richardson, Ben Allen, Germana Baldi et al. · Nucleic Acids Research · 2022 · 446 citations · Full text
Enzyme function prediction using contrastive learning
Haiyang Cui, Jianan Canal Li, Yunan Luo et al. · Science · 2023 · 392 citations
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263
Harvard University
Cambridge, United States
6 h-index
15 publications
824 citations
Columbia University
New York, United States
5 h-index
12 publications
1.2K citations
Hebrew University of Jerusalem
Jerusalem, Israel
4 h-index
14 publications
380 citations
Institute of Structural and Molecular Biology
London, United Kingdom
3 h-index
14 publications
438 citations
Technical University of Munich
Munich, Germany
3 h-index
15 publications
1.2K citations
George Mason University
Fairfax, United States
3 h-index
4 publications
195 citations
Nanjing University
Nanjing, China
3 h-index
5 publications
389 citations
Shandong University
Jinan, China
3 h-index
5 publications
123 citations
1–8 of 263
16 publications · 1.9K citations
15
h-index
10 publications · 678 citations
10
h-index
6 publications · 763 citations
6
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6 publications · 482 citations
6
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4 publications · 111 citations
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