Probability-based collaborative filtering model for predicting gene–disease associations

Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Download Full-text

An improved computational method for prediction of lncRNA-disease associations based on collaborative filtering and resource allocation

10.1109/kse53942.2021.9648632 ◽

2021 ◽

Author(s):

Van Tinh Nguyen ◽

Dang Hung Tran

Keyword(s):

Resource Allocation ◽

Collaborative Filtering ◽

Computational Method ◽

Disease Associations

Download Full-text

A neural collaborative filtering method for identifying miRNA-disease associations

Neurocomputing ◽

10.1016/j.neucom.2020.09.032 ◽

2021 ◽

Vol 422 ◽

pp. 176-185

Author(s):

Yue Liu ◽

Shu-Lin Wang ◽

Jun-Feng Zhang ◽

Wei Zhang ◽

Wen Li

Keyword(s):

Collaborative Filtering ◽

Filtering Method ◽

Disease Associations

Download Full-text

Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01616-5 ◽

2021 ◽

Vol 21 (S1) ◽

Author(s):

Ru Nie ◽

Zhengwei Li ◽

Zhu-hong You ◽

Wenzheng Bao ◽

Jiashu Li

Keyword(s):

Collaborative Filtering ◽

Disease Association ◽

Human Diseases ◽

Gaussian Kernel ◽

Predictive Score ◽

Disease Associations ◽

Similar Disease ◽

Hybrid Collaborative Filtering ◽

Case Validation ◽

The Impact

Abstract Background Accumulating studies indicates that microRNAs (miRNAs) play vital roles in the process of development and progression of many human complex diseases. However, traditional biochemical experimental methods for identifying disease-related miRNAs cost large amount of time, manpower, material and financial resources. Methods In this study, we developed a framework named hybrid collaborative filtering for miRNA-disease association prediction (HCFMDA) by integrating heterogeneous data, e.g., miRNA functional similarity, disease semantic similarity, known miRNA-disease association networks, and Gaussian kernel similarity of miRNAs and diseases. To capture the intrinsic interaction patterns embedded in the sparse association matrix, we prioritized the predictive score by fusing three types of information: similar disease associations, similar miRNA associations, and similar disease-miRNA associations. Meanwhile, singular value decomposition was adopted to reduce the impact of noise and accelerate predictive speed. Results We then validated HCFMDA with leave-one-out cross-validation (LOOCV) and two types of case studies. In the LOOCV, we achieved 0.8379 of AUC (area under the curve). To evaluate the performance of HCFMDA on real diseases, we further implemented the first type of case validation over three important human diseases: Colon Neoplasms, Esophageal Neoplasms and Prostate Neoplasms. As a result, 44, 46 and 44 out of the top 50 predicted disease-related miRNAs were confirmed by experimental evidence. Moreover, the second type of case validation on Breast Neoplasms indicates that HCFMDA could also be applied to predict potential miRNAs towards those diseases without any known associated miRNA. Conclusions The satisfactory prediction performance demonstrates that our model could serve as a reliable tool to guide the following research for identifying candidate miRNAs associated with human diseases.

Download Full-text

Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

BioMed Research International ◽

10.1155/2021/6652948 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Li Wang ◽

Cheng Zhong

Keyword(s):

Collaborative Filtering ◽

Cross Validation ◽

Kidney Neoplasms ◽

Feature Vector ◽

High Failure Rate ◽

Experimental Identification ◽

Disease Similarity ◽

Disease Associations ◽

Novel Method ◽

Fold Cross Validation

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.

Download Full-text