NRLMFβ : Beta-distribution-rescored neighborhood regularized logistic matrix factorization for improving the performance of drug-target interaction prediction

Biochemistry and Biophysics Reports
Tomohiro BanYutaka Akiyama

Abstract

Techniques for predicting interactions between a drug and a target (protein) are useful for strategic drug repositioning. Neighborhood regularized logistic matrix factorization (NRLMF) is one of the state-of-the-art drug-target interaction prediction methods; it is based on a statistical model using the Bernoulli distribution. However, the prediction is not accurate when drug-target interaction pairs have less interaction information (e.g., the sum of the number of ligands for a target and the number of target proteins for a drug). This study aimed to address this issue by proposing NRLMF with beta distribution rescoring (NRLMFβ), which is an algorithm to improve the score of NRLMF. The score of NRLMFβ is equivalent to the value of the original NRLMF score when the concentration of the beta distribution becomes infinity. The beta distribution is known as a conjugative prior distribution of the Bernoulli distribution and can reflect the amount of interaction information to its shape based on Bayesian inference. Therefore, in NRLMFβ, the beta distribution was used for rescoring the NRLMF score. In the evaluation experiment, we measured the average values of area under the receiver operating characteristics and area under precisio...Continue Reading

References

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Citations

Jan 18, 2020·Briefings in Bioinformatics·Maryam BagherianKayvan Najarian
Mar 19, 2020·Briefings in Bioinformatics·Maryam BagherianKayvan Najarian
Apr 23, 2020·Frontiers in Bioengineering and Biotechnology·Xianfang WangDongqing Wei

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Methods Mentioned

BETA
interaction prediction

Software Mentioned

TSUBAME
SuperTarget
ECkNN
SIMCOMP
BRENDA
WNNGIP

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