2016
DOI: 10.1186/s13059-016-0989-x
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MUFFINN: cancer gene discovery via network analysis of somatic mutation data

Abstract: A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation d… Show more

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Cited by 137 publications
(160 citation statements)
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“…We compared the 12 prioritizing results with those obtained by DNmax and DNsum (two algorithms in MUFFINN)21 using the same data and the same five reference cancer gene sets, that is, CGC (Cancer Genome Census),26 CGCpointMut, Rule2020,5 HCD,27 and MouseMut28, 29 (see the Supporting Information for details), with CGC being the most well‐known and confident cancer gene set. Both ROC curves ( Figure 2 a) and AUC (area under the ROC curve) scores (Figure 2b) show that MaxMIF outperforms DNmax and DNsum in the AWG Pan‐Cancer dataset, using either the HumanNet or STRINGv10 networks validated on the CGC reference cancer gene set.…”
Section: Resultsmentioning
confidence: 99%
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“…We compared the 12 prioritizing results with those obtained by DNmax and DNsum (two algorithms in MUFFINN)21 using the same data and the same five reference cancer gene sets, that is, CGC (Cancer Genome Census),26 CGCpointMut, Rule2020,5 HCD,27 and MouseMut28, 29 (see the Supporting Information for details), with CGC being the most well‐known and confident cancer gene set. Both ROC curves ( Figure 2 a) and AUC (area under the ROC curve) scores (Figure 2b) show that MaxMIF outperforms DNmax and DNsum in the AWG Pan‐Cancer dataset, using either the HumanNet or STRINGv10 networks validated on the CGC reference cancer gene set.…”
Section: Resultsmentioning
confidence: 99%
“…To further evaluate MaxMIF's ability to identify responsible drive genes, we compared it with five well‐regarded methods Mutation_Assessor (Mut_Ass),7 MutSig2.0,11 MutSigCV,12 ContrastRank,20 and MUFFINN21 using somatic mutation datasets from 19 cancer types (see the details in Table S2, Supporting Information). Since ContrastRank is targeted at colon cancer (COAD), lung cancer (LUAD), and prostate adenocarcinomas (PRAD), we excluded it when the comparison was based on the average performance across the 19 cancer types, and further compared it with MaxMIF on the two common cancer cohorts (Figure S14, Supporting Information).…”
Section: Resultsmentioning
confidence: 99%
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