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Recent advances on the machine learning methods in predicting ncRNA-protein interactions

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Abstract

Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that most ncRNAs usually work by interacting with the corresponding RNA-binding proteins. Therefore, ncRNA-protein interaction is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, machine-learning methods for predicting ncRNA-protein interactions are increasingly favored by the researchers. In this review, we summarize several machine learning predictive models of ncRNA-protein interactions over the past few years, and briefly describe the characteristics of these machine learning models. In order to optimize the performance of machine learning models to better predict ncRNA-protein interactions, we give some promising future computational directions at the end.

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Abbreviations

ncRNA:

Non-coding RNA

rRNA:

Ribosomal RNA

tRNA:

Transfer RNA

miRNA:

MicroRNA

snRNA:

Small nuclear RNA

lncRNAs:

Long non coding RNAs

SVM:

Support vector machine

RF:

Random forest

LOOCV:

Leave-one-out cross validation

K-CV:

K-Fold Cross Validation

ROC:

Receiver operator characteristics

AUC:

The area under ROC curve

PPSNs:

Protein–protein similarity networks

SNF:

Similarity network fusion

XGB:

Extreme gradient enhancement

SAN:

Stacking autoencoder networks

PSSM:

Position-specific scoring matrix

SVD:

Singular value decomposition

PZM:

Pseudo-Zernike moment

PCC:

Pearson correlation coefficient

GBDT:

Gradient boosting decision tree

Extra tree:

Extremely randomized trees

LMs:

Legendre moments

PWM:

Position weight matrix

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Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 11805091.

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Author notes

  1. Lin Zhong and Meiqin Zhen contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics, Liaoning University, Shenyang, 110036, China

    Lin Zhong

  2. Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China

    Meiqin Zhen

  3. School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China

    Jianqiang Sun

  4. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

    Qi Zhao

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  1. Lin Zhong
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  4. Qi Zhao
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Contributions

QZ designed and conceived the project. LZ, JS and QZ wrote the manuscript. MZ designed the figures and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qi Zhao.

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The authors declare no conficts of interest and in particular: Lin Zhong declares that she has no confict of interest, Meiqin Zhen declares that she has no confict of interest, Jianqiang Sun declares that he has no confict of interest, Qi Zhao declares that he has no confict of interest.

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Zhong, L., Zhen, M., Sun, J. et al. Recent advances on the machine learning methods in predicting ncRNA-protein interactions. Mol Genet Genomics 296, 243–258 (2021). https://doi.org/10.1007/s00438-020-01727-0

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