Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration
| العنوان: | Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration |
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| المؤلفون: | Caihua Wang, Juan Liu, Guangsheng Wu |
| المصدر: | BMC Medical Genomics BMC Medical Genomics, Vol 10, Iss S5, Pp 17-30 (2017) |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0301 basic medicine, lcsh:Internal medicine, Similarity (geometry), lcsh:QH426-470, Relation (database), Computer science, computer.software_genre, Similarity, 03 medical and health sciences, Cut, Drug Discovery, Genetics, Computer Graphics, Humans, Disease, Layer (object-oriented design), lcsh:RC31-1245, Genetics (clinical), Drug-disease interaction, Guilt-by-association, business.industry, Research, Drug Repositioning, Computational Biology, Pattern recognition, lcsh:Genetics, Identification (information), 030104 developmental biology, Node (circuits), Enhanced Data Rates for GSM Evolution, Artificial intelligence, business, Transcriptome, computer, Algorithms, Graph cut, Data integration, Integration strategy |
| الوصف: | Background Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. Methods In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. Results By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC. Conclusions The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations. |
| تدمد: | 1755-8794 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3b68a559fd76ae8469789b7df847cc1 https://pubmed.ncbi.nlm.nih.gov/29297383 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....e3b68a559fd76ae8469789b7df847cc1 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17558794 |
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