Abstract:Increasing evidences show that the abnormal microRNA (miRNA) expression is related to a variety of complex human diseases. However, the current biological experiments to determine miRNA-disease associations are time consuming and expensive. Therefore, computational models to predict potential miRNA-disease associations are in urgent need. Though many miRNA-disease association prediction methods have been proposed, there is still a room to improve the prediction accuracy. In this paper, we propose a matrix comp… Show more
“…In this study, the predictive capability of EMCMDA is assessed through Global LOOCV and 5-fold CV using the benchmark dataset. To assess the proposed model, we compared its predictions with those generated by HGCLAMIR 16 , BNNRMDA 24 , WBNPMD 19 , KATZBNRA 18 , PMFMDA 25 , IMCMDA 26 . Figure 2 Global LOOCV and 5-fold CV were employed on the benchmark dataset to compare the predictive capabilities of various models.…”
Section: Resultsmentioning
confidence: 99%
“…However, it lacks interpretability. BNNRMDA 24 employs bounded kernel paradigm regularization for predicting potential MDAs. Its innovation lies in constraining the prediction structure to the interval of 0-1, ensuring interpretability of predictions.…”
Section: Discussionmentioning
confidence: 99%
“…Matrix completion, a viable approach employed in predicting MDAs, has garnered widespread recognition for its practicality and effectiveness. Chen et al 24 introduced a novel technique for forecasting MDAs using bounded nuclear norm regularization (BNNRMDA). Initially, they utilized valuable information on miRNAs and diseases to construct a diverse network.…”
Section: Introductionmentioning
confidence: 99%
“…. Following this, the ADMM-based framework is employed for solving equation (24). Experimental validation shows that k = 1 produces the best result.…”
Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.
“…In this study, the predictive capability of EMCMDA is assessed through Global LOOCV and 5-fold CV using the benchmark dataset. To assess the proposed model, we compared its predictions with those generated by HGCLAMIR 16 , BNNRMDA 24 , WBNPMD 19 , KATZBNRA 18 , PMFMDA 25 , IMCMDA 26 . Figure 2 Global LOOCV and 5-fold CV were employed on the benchmark dataset to compare the predictive capabilities of various models.…”
Section: Resultsmentioning
confidence: 99%
“…However, it lacks interpretability. BNNRMDA 24 employs bounded kernel paradigm regularization for predicting potential MDAs. Its innovation lies in constraining the prediction structure to the interval of 0-1, ensuring interpretability of predictions.…”
Section: Discussionmentioning
confidence: 99%
“…Matrix completion, a viable approach employed in predicting MDAs, has garnered widespread recognition for its practicality and effectiveness. Chen et al 24 introduced a novel technique for forecasting MDAs using bounded nuclear norm regularization (BNNRMDA). Initially, they utilized valuable information on miRNAs and diseases to construct a diverse network.…”
Section: Introductionmentioning
confidence: 99%
“…. Following this, the ADMM-based framework is employed for solving equation (24). Experimental validation shows that k = 1 produces the best result.…”
Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.
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