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• W2966497472 abstract "A large number of biomedical ontologies are created to provide controlled vocabularies for sharing biomedical knowledge in different biomedical domains. Quantitative measurement of disease associations based on biomedical ontologies could provide supports for discovering similar diseases caused by similar molecular process, which is beneficial to improve the corresponding medical diagnosis and treatment. Therefore, we deal with the effective measure of disease similarities in this paper. In particular, we propose a novel regression model based on the deep neural network (DNN) to improve the evaluation of similarities among diseases. We firstly extract the feature vectors of disease pairs, and then train the DNN-based regression model that learns from the information of training set to simulate the complex non-linear relationship among disease pairs. Finally, a comprehensive experimental evaluation is carried out to show the advantages as a solution for measuring disease similarities in terms of the receiver operating characteristic curve (ROC) and the precision-recall curve (PRC)." @default.
• W2966497472 created "2019-08-13" @default.
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• W2966497472 date "2019-01-01" @default.
• W2966497472 modified "2023-10-18" @default.
• W2966497472 title "An Effective Approach of Measuring Disease Similarities Based on the DNN Regression Model" @default.
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• W2966497472 doi "https://doi.org/10.1007/978-3-030-26969-2_19" @default.
• W2966497472 hasPublicationYear "2019" @default.
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