MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information

Dai, Qiuying; Chu, Yanyi; Li, Zhiqi; Zhao, Yusong; Mao, Xueying; Wang, Yanjing; Xiong, Yi; Wei, Dong‐Qing

doi:10.1016/j.compbiomed.2021.104706

Cited by 27 publications

(9 citation statements)

References 113 publications

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“…The drug feature encoder module mainly includes multi-head self-attention layers and an autoencoder. The multi-head self-attention layers can focus on more important drug features [ 40 , 41 ], and further the autoencoder performs feature dimensionality reduction [ 42 , 43 ]. Consequently, lower-dimensional and better drug representations can be obtained through the drug feature encoder module.…”

Section: Methodsmentioning

confidence: 99%

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin

Chen

et al. 2022

J Cheminform

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The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classification loss module. The drug feature encoder and mean squared error loss module uses self-attention mechanism and autoencoder to learn drug-level latent features. The drug latent feature fusion and supervised contrastive loss module uses multi-scale feature fusion to learn drug pair-level latent features. The prediction and classification loss module predicts DDI types of each drug pair. We evaluate MDDI-SCL on three different tasks of two datasets. Experimental results demonstrate that MDDI-SCL achieves better or comparable performance as the state-of-the-art methods. Furthermore, the effectiveness of supervised contrastive learning is validated by ablation experiment, and the feasibility of MDDI-SCL is supported by case studies. The source codes are available at https://github.com/ShenggengLin/MDDI-SCL.

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Section: Methodsmentioning

confidence: 99%

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

Lin

Chen

et al. 2022

J Cheminform

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“…On the other hand, with artificial intelligence technology becoming more widely used, more and more machine learning approaches are being applied to the field of miRNA-disease association prediction. 16 , 17 For example, Zhou et al. 18 proposed a gradient-enhanced decision tree combined with a logistic regression model to predict miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction

Zou,

Ji,

Zhang

et al. 2024

Molecular Therapy - Nucleic Acids

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“…In addition, MDA-GCNFTG could predict not only new MDAs but also hidden association between diseases without known related miRNAs and miRNAs without known related diseases. Dai et al [18] proposed a model for identifying potential MDAs based on the cascade forest model (MDA-CF), which integrated multi-source information to comprehensively characterize miRNAs and diseases, and used autoencoders for dimensionality reduction to obtain the optimal feature space and ranked it. A joint forest model was used in the prediction of potential MDAs.…”

Section: Introductionmentioning

confidence: 99%

BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network

Shang

Yang

et al. 2022

BMC Genomics

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Background MicroRNAs (miRNAs) have been confirmed to be inextricably linked to the emergence of human complex diseases. The identification of the disease-related miRNAs has gradually become a routine way to unveil the genetic mechanisms of examined disorders. Methods In this study, a method BLNIMDA based on a weighted bi-level network was proposed for predicting hidden associations between miRNAs and diseases. For this purpose, the known associations between miRNAs and diseases as well as integrated similarities between miRNAs and diseases are mapped into a bi-level network. Based on the developed bi-level network, the miRNA-disease associations (MDAs) are defined as strong associations, potential associations and no associations. Then, each miRNA-disease pair (MDP) is assigned two information properties according to the bidirectional information distribution strategy, i.e., associations of miRNA towards disease and vice-versa. Finally, two affinity weights for each MDP obtained from the information properties and the association type are then averaged as the final association score of the MDP. Highlights of the BLNIMDA lie in the definition of MDA types, and the introduction of affinity weights evaluation from the bidirectional information distribution strategy and defined association types, which ensure the comprehensiveness and accuracy of the final prediction score of MDAs. Results Five-fold cross-validation and leave-one-out cross-validation are used to evaluate the performance of the BLNIMDA. The results of the Area Under Curve show that the BLNIMDA has many advantages over the other seven selected computational methods. Furthermore, the case studies based on four common diseases and miRNAs prove that the BLNIMDA has good predictive performance. Conclusions Therefore, the BLNIMDA is an effective method for predicting hidden MDAs.

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MDA-CF: Predicting MiRNA-Disease associations based on a cascade forest model by fusing multi-source information

Cited by 27 publications

References 113 publications

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning

MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction

BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network

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