Computational Variant Prediction
Computational Variant Prediction is a scholarly methodological contribution that employs algorithmic, statistical, or machine learning approaches to infer the functional or clinical impact of genetic variants, distinguished from other variant classification strategies by its reliance on in silico modeling rather than empirical or population-based evidence.
203
Publications
29.9K
Citations
1.2K
Authors
610
Institutions
Publications per year
2017–2026
128
Parents
203
CADD: predicting the deleteriousness of variants throughout the human genome
Philipp Rentzsch, Daniela Witten, Gregory M. Cooper et al. · Nucleic Acids Research · 2018 · 3.9K citations · Full text
1–5 of 203
1.2K
9 publications · 922 citations
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Rutgers, The State University of New Jersey, United States
7 publications · 2K citations
7
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1–5 of 1.2K
610
Inserm
Paris, France
12 h-index
33 publications
1.7K citations

University of Edinburgh
Edinburgh, United Kingdom
10 h-index
22 publications
1.8K citations

Lund University
Lund, Sweden
9 h-index
16 publications
1.4K citations
Harvard University
Cambridge, United States
8 h-index
33 publications
5.8K citations

Centre National de la Recherche Scientifique
Paris, France
8 h-index
45 publications
1.7K citations

Broad Institute
Cambridge, United States
8 h-index
26 publications
3.4K citations

University of Bologna
Bologna, Italy
7 h-index
15 publications
3.4K citations
Cardiff University
Cardiff, United Kingdom
7 h-index
13 publications
5.7K citations
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22 publications · 2.7K citations
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13 publications · 6.2K citations
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13 publications · 764 citations
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8 publications · 1.3K citations
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6 publications · 435 citations
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