Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306361925> ?p ?o ?g. }
- W4306361925 abstract "Abstract Background Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. Methods In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. Results Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. Conclusions NeRD’s feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment." @default.
- W4306361925 created "2022-10-17" @default.
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- W4306361925 date "2022-10-17" @default.
- W4306361925 modified "2023-10-16" @default.
- W4306361925 title "NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data" @default.
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- W4306361925 cites W2043398720 @default.
- W4306361925 cites W2044433401 @default.
- W4306361925 cites W2052705131 @default.
- W4306361925 cites W2065835133 @default.
- W4306361925 cites W2087312216 @default.
- W4306361925 cites W2094205631 @default.
- W4306361925 cites W2102800752 @default.
- W4306361925 cites W2108068107 @default.
- W4306361925 cites W2108933868 @default.
- W4306361925 cites W2113523455 @default.
- W4306361925 cites W2124132332 @default.
- W4306361925 cites W2128728535 @default.
- W4306361925 cites W2129040595 @default.
- W4306361925 cites W2129860849 @default.
- W4306361925 cites W2139149338 @default.
- W4306361925 cites W2158025958 @default.
- W4306361925 cites W2166999544 @default.
- W4306361925 cites W2171756499 @default.
- W4306361925 cites W2173073476 @default.
- W4306361925 cites W2205607240 @default.
- W4306361925 cites W2219715551 @default.
- W4306361925 cites W2294990791 @default.
- W4306361925 cites W2432718434 @default.
- W4306361925 cites W2501016862 @default.
- W4306361925 cites W2513486311 @default.
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- W4306361925 cites W2767281692 @default.
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- W4306361925 cites W2789876780 @default.
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- W4306361925 cites W2885938058 @default.
- W4306361925 cites W2888554793 @default.
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- W4306361925 cites W2921948547 @default.
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- W4306361925 doi "https://doi.org/10.1186/s12916-022-02549-0" @default.
- W4306361925 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36244991" @default.
- W4306361925 hasPublicationYear "2022" @default.
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