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- W3137236530 abstract "The detection and characterization of somatic mutations have become the important means to analyze the occurrence and development of cancer and, ultimately, will help to select effective and precise treatment for specific cancer patients. It is very difficult to detect somatic mutations accurately from the massive sequencing data. In this paper, a forest-graph-embedded deep feed-forward network (forgeNet) is utilized to detect somatic mutations from the sequencing data. In forgeNet, the random forest (RF) or Gradient Boosting Machine (GBM) and graph-embedded deep feed-forward network (GEDFN) are utilized to extract features and implement classification, respectively. Three real somatic mutation datasets collected from 48 triple-negative breast cancers are utilized to test the somatic mutation detection performances of forgeNet. The detection results show that forgeNet could make the 0.05%–0.424% improvements in terms of area under the curve (AUC) compared with support vector machines and random forest." @default.
- W3137236530 created "2021-03-29" @default.
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- W3137236530 date "2021-03-19" @default.
- W3137236530 modified "2023-09-25" @default.
- W3137236530 title "Deep Neural Network for Somatic Mutation Classification" @default.
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- W3137236530 doi "https://doi.org/10.1155/2021/5529202" @default.
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