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• W4313480822 abstract "Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA." @default.
• W4313480822 created "2023-01-06" @default.
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• W4313480822 date "2023-01-01" @default.
• W4313480822 modified "2023-10-18" @default.
• W4313480822 title "MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm" @default.
• W4313480822 cites W1607743621 @default.
• W4313480822 cites W1995857191 @default.
• W4313480822 cites W2008491586 @default.
• W4313480822 cites W2088786536 @default.
• W4313480822 cites W2106029302 @default.
• W4313480822 cites W2119687825 @default.
• W4313480822 cites W2145336165 @default.
• W4313480822 cites W2148668736 @default.
• W4313480822 cites W2153280946 @default.
• W4313480822 cites W2167932086 @default.
• W4313480822 cites W2173913502 @default.
• W4313480822 cites W2322763599 @default.
• W4313480822 cites W2397934608 @default.
• W4313480822 cites W2494944008 @default.
• W4313480822 cites W2497274407 @default.
• W4313480822 cites W2544493586 @default.
• W4313480822 cites W2570516417 @default.
• W4313480822 cites W2581871052 @default.
• W4313480822 cites W2583889943 @default.
• W4313480822 cites W2624752195 @default.
• W4313480822 cites W2738095712 @default.
• W4313480822 cites W2739664659 @default.
• W4313480822 cites W2775092768 @default.
• W4313480822 cites W2776589520 @default.
• W4313480822 cites W2787055217 @default.
• W4313480822 cites W2792533056 @default.
• W4313480822 cites W2803434593 @default.
• W4313480822 cites W2806774580 @default.
• W4313480822 cites W2807176368 @default.
• W4313480822 cites W2885265830 @default.
• W4313480822 cites W2885494203 @default.
• W4313480822 cites W2897174088 @default.
• W4313480822 cites W2897440086 @default.
• W4313480822 cites W2899728356 @default.
• W4313480822 cites W2901312953 @default.
• W4313480822 cites W2922817064 @default.
• W4313480822 cites W2936110251 @default.
• W4313480822 cites W2960557205 @default.
• W4313480822 cites W2963055407 @default.
• W4313480822 cites W2974724150 @default.
• W4313480822 cites W2979072963 @default.
• W4313480822 cites W2980767508 @default.
• W4313480822 cites W2981213486 @default.
• W4313480822 cites W2981398951 @default.
• W4313480822 cites W2981584921 @default.
• W4313480822 cites W3007573310 @default.
• W4313480822 cites W3021586058 @default.
• W4313480822 cites W3026381352 @default.
• W4313480822 cites W3041880399 @default.
• W4313480822 cites W3045879648 @default.
• W4313480822 cites W3082810497 @default.
• W4313480822 cites W3092006129 @default.
• W4313480822 cites W3094448726 @default.
• W4313480822 cites W3118669798 @default.
• W4313480822 cites W3119962804 @default.
• W4313480822 cites W3127559895 @default.
• W4313480822 cites W3159209990 @default.
• W4313480822 cites W3159447716 @default.
• W4313480822 cites W3163306106 @default.
• W4313480822 cites W3179748251 @default.
• W4313480822 cites W3192134500 @default.
• W4313480822 cites W3192850761 @default.
• W4313480822 cites W3194482250 @default.
• W4313480822 cites W3196100602 @default.
• W4313480822 cites W3197858765 @default.
• W4313480822 cites W3201821663 @default.
• W4313480822 cites W4206501708 @default.
• W4313480822 cites W4206547282 @default.
• W4313480822 cites W4223957045 @default.
• W4313480822 cites W4224254299 @default.
• W4313480822 cites W4255502791 @default.
• W4313480822 cites W4281685099 @default.
• W4313480822 cites W4283269373 @default.
• W4313480822 cites W4293065402 @default.
• W4313480822 cites W4296919006 @default.
• W4313480822 doi "https://doi.org/10.1016/j.csbj.2022.12.053" @default.
• W4313480822 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36824227" @default.
• W4313480822 hasPublicationYear "2023" @default.
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