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Application of Matrix Tri-Factorization for Predicting miRNA-Disease Associations

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 914)

Abstract

Recent research on miRNAs has shown that miRNAs play an important role in disease progression, leading to the investigation of the discovery of miRNA-disease affiliation. For predicting miRNA-disease affiliation, matrix tri-factorization is used. The functional similarity of miRNA, the similarity of miRNA based solely on diseases, the similarity of miRNA based solely on environmental factors, and adjacency matrix for miRNA and disease had been calculated and considered in the proposed approach. We also took the semantic similarity of the diseases into account. We tested the proposed method on eight diseases; the method is robust, and the results of the experiments ranked 50 miRNAs for gastric cancers, 48 miRNAs for breast cancers, and 48 miRNAs for kidney cancers with in the top 50 predictions. We obtained the area under the curve of 0.90967.

Keywords

  • Matrix tri-factorization
  • miRNA functional similarity
  • Disease semantic similarity

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Rashmi, J.R., Rangarajan, L. (2022). Application of Matrix Tri-Factorization for Predicting miRNA-Disease Associations. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_6

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  • DOI: https://doi.org/10.1007/978-981-19-2980-9_6

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