Synthetic lethality and cancer is a research paper published in Nature Reviews Genetics (2017). On theSindex it has a DataRank of 0.982. It has been cited 695 times.
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Base Score Contribution
0.982
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
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Grant: unidentified
unidentified
FWCI
22.40
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Computational inference of cancer-specific vulnerabilities in clinical samples
Additional file 1 of Computational inference of cancer-specific vulnerabilities in clinical samples
Additional file 9 of Computational inference of cancer-specific vulnerabilities in clinical samples
Additional file 9 of Computational inference of cancer-specific vulnerabilities in clinical samples
Additional file 10 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 10 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 11 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 11 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 2 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 2 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 4 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 4 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 5 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 5 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 6 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 6 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 7 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 7 of CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens
Additional file 1 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer
Additional file 1 of A benchmark study of deep learning-based multi-omics data fusion methods for cancer