PMAMCA

Prediction of microRNA-disease association utilizing a matrix completion approach

Jihwan Ha, Chihyun Park, Sang Hyun Park

Research output: Contribution to journalArticle

Abstract

Background: Numerous experimental results have indicated that microRNAs (miRNAs) play a vital role in biological processes, as well as outbreaks of diseases at the molecular level. Despite their important role in biological processes, knowledge regarding specific functions of miRNAs in the development of human diseases is very limited. While attempting to solve this problem, many computational approaches have been proposed and attracted significant attention. However, most previous approaches suffer from the common problem of being inapplicable to new diseases without any known miRNA-disease associations. Results: This paper proposes a novel method for inferring disease-miRNA associations utilizing a machine learning technique called matrix factorization, which is widely used in recommendation systems. In recommendation systems, the goal is to predict rating scores that a user might assign to specific items. By replacing users with miRNAs and items with diseases, we can efficiently predict miRNA-disease associations without seed miRNAs. As a result, our proposed model, called prediction of microRNA-disease association utilizing a matrix completion approach, achieves excellent performance compared to previous approaches with a reliable AUC value of 0.882 by implementing five-fold cross validation. Conclusions: To the best of our knowledge, the proposed method applies the matrix completion technique to infer miRNA-disease associations and overcome the seed-miRNA problem negatively affects existing computational models.

Original languageEnglish
Article number33
JournalBMC Systems Biology
Volume13
Issue number1
DOIs
Publication statusPublished - 2019 Mar 20

Fingerprint

Matrix Completion
MicroRNA
MicroRNAs
Prediction
Biological Phenomena
Recommender systems
Recommendation System
Seed
Seeds
Predict
Matrix Factorization
Factorization
Human Development
Learning systems
Cross-validation
Computational Model
Prediction Model
Area Under Curve
Disease Outbreaks
Assign

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Modelling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

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abstract = "Background: Numerous experimental results have indicated that microRNAs (miRNAs) play a vital role in biological processes, as well as outbreaks of diseases at the molecular level. Despite their important role in biological processes, knowledge regarding specific functions of miRNAs in the development of human diseases is very limited. While attempting to solve this problem, many computational approaches have been proposed and attracted significant attention. However, most previous approaches suffer from the common problem of being inapplicable to new diseases without any known miRNA-disease associations. Results: This paper proposes a novel method for inferring disease-miRNA associations utilizing a machine learning technique called matrix factorization, which is widely used in recommendation systems. In recommendation systems, the goal is to predict rating scores that a user might assign to specific items. By replacing users with miRNAs and items with diseases, we can efficiently predict miRNA-disease associations without seed miRNAs. As a result, our proposed model, called prediction of microRNA-disease association utilizing a matrix completion approach, achieves excellent performance compared to previous approaches with a reliable AUC value of 0.882 by implementing five-fold cross validation. Conclusions: To the best of our knowledge, the proposed method applies the matrix completion technique to infer miRNA-disease associations and overcome the seed-miRNA problem negatively affects existing computational models.",
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PMAMCA : Prediction of microRNA-disease association utilizing a matrix completion approach. / Ha, Jihwan; Park, Chihyun; Park, Sang Hyun.

In: BMC Systems Biology, Vol. 13, No. 1, 33, 20.03.2019.

Research output: Contribution to journalArticle

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