Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA–Disease Associations Prediction

Wang, Shudong; Li, YunYin; Zhang, Yuanyuan; Pang, Shanchen; Qiao, Sibo; Zhang, Y.; Wang, Fuyu

doi:10.1093/bib/bbad270

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…These known associations are derived from a large number of experimental supporting evidence of miRNA‐disease interactions network. This article references the HMDD v2.0 collated in MSHGANMDA 38 and the HMDD v3.2 collated in GAMCNMDF 39 …”

Section: Resultsmentioning

confidence: 99%

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DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

Jia,

Wang,

Xing

et al. 2024

Numer Methods Biomed Eng

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MiRNA (microRNA)‐disease association prediction has essential applications for early disease screening. The process of traditional biological experimental validation is both time‐consuming and expensive. However, as artificial intelligence technology continues to advance, computational methods have become efficient tools for predicting miRNA‐disease associations. These methods often rely on the combination of multiple sources of association data and require improved feature mining. This study proposes a dynamic graph attention‐based association prediction model, DGAMDA, which combines feature mapping and dynamic graph attention mechanisms through feature mining on a single miRNA‐disease association network. DGAMDA effectively solves the problems of feature heterogeneity and inadequate feature mining by previous static graph attention mechanisms and achieves high‐precision feature mining and association scoring prediction. We conducted a five‐fold cross‐validation experiment and obtained the mean values of Accuracy, Precision, Recall, and F1‐score, which were .8986, .8869, .9115, and .8984, respectively. Our proposed model outperforms other advanced models in terms of experimental results, demonstrating its effectiveness in feature mining and association prediction based on a single association network. In addition, our model can also be used to predict miRNAs associated with unknown diseases.

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Section: Resultsmentioning

confidence: 99%

“…This article references the HMDD v2.0 collated in MSHGANMDA 38 and the HMDD v3.2 collated in GAMCNMDF. 39…”

Section: Databasementioning

confidence: 99%

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

Jia,

Wang,

Xing

et al. 2024

Numer Methods Biomed Eng

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“…They combine integrated multi-view representation and hypergraph contrast learning techniques with view-aware attention mechanisms to forecast MDAs. Wang et al 17 presented a GAMCNMDF approach. They established an antagonistic matrix-complete network that interconnects miRNAs and diseases, which was subsequently indicated as a matrix.…”

Section: Introductionmentioning

confidence: 99%

EMCMDA: predicting miRNA-disease associations via efficient matrix completion

Qin,

Zhang,

2024

Sci Rep

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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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Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Krishnan,

Roy,

Gromiha

2024

Briefings in Bioinformatics

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Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

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Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA–Disease Associations Prediction

Cited by 3 publications

References 42 publications

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

DGAMDA: Predicting miRNA‐disease association based on dynamic graph attention network

EMCMDA: predicting miRNA-disease associations via efficient matrix completion

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

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