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- W4313452908 abstract "Predicting disease-related long non-coding RNAs (lncRNAs) can help reveal the genetic mechanisms of complex diseases. Accurately identifying disease-associated lncRNAs is crucial for human diagnosis and therapeutics of complex diseases. However, most computational models ignore the noise of the data and the interference of redundant information. In this study, we build heterogeneous networks by integrating three different data sources of lncRNAs, miRNAs and diseases, and then propose an efficient computational model called MHILDA. MHILDA selects the most helpful features to train the model by Lasso's feature extraction. MHILDA is evaluated by five-fold cross-validation and performs well both on the benchmark dataset and on the independent test set. To further evaluate the performance of MHILDA, two types of case studies are implemented. The experimental results show that MHILDA can predict lncRNAs for unknown diseases." @default.
- W4313452908 created "2023-01-06" @default.
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- W4313452908 date "2022-12-06" @default.
- W4313452908 modified "2023-09-24" @default.
- W4313452908 title "MHILDA: identifying disease-associated lncRNAs by extracting key features from integrated heterogeneous networks" @default.
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- W4313452908 doi "https://doi.org/10.1109/bibm55620.2022.9995434" @default.
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