Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

Li, Jinlong; Chen, Xingyu; Huang, Qixing; Wang, Yang; Xie, Yuanfu; Dai, Zong; Zou, Xiaoyong; Li, Zhanchao

doi:10.1038/s41598-020-75005-9

Cited by 12 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that unlabeled samples may contain potential positive samples (i.e., DDIs that have not been verified by experiments), randomly selecting negative samples from unlabeled ones may affect the prediction performance of the model. Therefore, the positive unlabeled learning method proposed by Li et al [ 50 ] was adopted to select reliable negative samples. Steps are as follows: (1) use the fingerprint feature of the drug to calculate the mean of each dimension in the positive samples and form a 2048-dimensional feature vector (i.e., cluster center); (2) calculate the Euclidean distance between all unlabeled samples and the cluster center, and the average Euclidean distance (AED); (3) set a threshold D (D = n × AED, n ∈ ℝ + ), and an unlabeled sample can be regarded as a reliable negative sample if the distance from the unlabeled one to the cluster center is higher than the threshold.…”

Section: Methodsmentioning

confidence: 99%

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions

et al. 2023

Self Cite

View full text Add to dashboard Cite

The identification of drug–drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

show abstract

Section: Methodsmentioning

confidence: 99%

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…They constructed the multi-network of miRNA, long non-coding RNA (lncRNA) and disease to propagate label that could be utilized to infer unknown interaction information of miRNA-disease. Recently, Li et al [ 24 ] predicted miRNA-disease connections by utilizing the modified random forest algorithm that relied on sequence information and symptom information in Seq-SymRF model. Moreover, Euclidean distance-based clustering method was applied to choose reliable negative samples in this model.…”

Section: Introductionmentioning

confidence: 99%

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

Wang

et al. 2021

PLoS Comput Biol

View full text Add to dashboard Cite

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolution autoencoder (GCAE) is utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performance were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA were beneficial to infer potential associations of miRNA-disease.

show abstract

DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

Wei,

Wang,

Gao

et al. 2024

Front. Comput. Sci.

View full text Add to dashboard Cite

MicroRNAs (miRNAs) are closely related to numerous complex human diseases, therefore, exploring miRNA-disease associations (MDAs) can help people gain a better understanding of complex disease mechanism. An increasing number of computational methods have been developed to predict MDAs. However, the sparsity of the MDAs may hinder the performance of many methods. In addition, many methods fail to capture the nonlinear relationships of miRNA-disease network and inadequately leverage the features of network and neighbor nodes. In this study, we propose a deep matrix factorization model with variational autoencoder (DMFVAE) to predict potential MDAs. DMFVAE first decomposes the original association matrix and the enhanced association matrix, in which the enhanced association matrix is enhanced by self-adjusting the nearest neighbor method, to obtain sparse vectors and dense vectors, respectively. Then, the variational encoder is employed to obtain the nonlinear latent vectors of miRNA and disease for the sparse vectors, and meanwhile, node2vec is used to obtain the network structure embedding vectors of miRNA and disease for the dense vectors. Finally, sample features are acquired by combining the latent vectors and network structure embedding vectors, and the final prediction is implemented by convolutional neural network with channel attention. To evaluate the performance of DMFVAE, we conduct five-fold cross validation on the HMDD v2.0 and HMDD v3.2 datasets and the results show that DMFVAE performs well. Furthermore, case studies on lung neoplasms, colon neoplasms, and esophageal neoplasms confirm the ability of DMFVAE in identifying potential miRNAs for human diseases.

show abstract

Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

Cited by 12 publications

References 33 publications

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug–Drug Interactions

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder

Contact Info

Product

Resources

About