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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...

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mirMachine: A One-Stop Shop for Plant miRNA Annotation

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Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm.

Shudong Wang¹, Tiyao Liu¹, Chuanru Ren¹

¹College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China.

Briefings in Bioinformatics

|June 27, 2023

Summary

This study introduces a novel method, the truncated Schatten p-norm (TSPN), for predicting associations between small molecules (SMs) and microRNAs (miRNAs). TSPN improves accuracy by better approximating rank functions, outperforming existing models in disease association predictions.

Keywords:

association prediction matrix completion microRNA small molecule truncated schatten p-norm

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Area of Science:

Biomedical Informatics
Computational Biology
Genomics

Background:

MicroRNAs (miRNAs) are crucial in human diseases and can be targeted by small molecules (SMs).
Existing SM-miRNA association prediction models struggle to fully capture SM/miRNA similarity.
Current matrix completion methods use nuclear norm, which has limitations compared to rank functions.

Purpose of the Study:

To develop an advanced model for predicting SM-miRNA associations.
To enhance the accuracy of SM-miRNA similarity identification.
To overcome the drawbacks of traditional matrix completion techniques in this domain.

Main Methods:

Preprocessing SM/miRNA similarity using Gaussian interaction profile kernel similarity.
Constructing a heterogeneous SM-miRNA network from three biological matrices.
Developing a prediction model using the truncated Schatten p-norm (TSPN) and an iterative algorithm.
Employing weighted singular value shrinkage to refine the model.

Main Results:

TSPN identified more SM/miRNA similarities, significantly improving prediction accuracy.
The model demonstrated superior performance compared to advanced methods across multiple cross-validation experiments.
Extensive validation through case studies confirmed numerous predictive associations, supported by public literature.

Conclusions:

TSPN offers a more accurate and reliable approach for predicting SM-miRNA associations.
The method effectively addresses limitations of existing models by better approximating rank functions.
TSPN holds significant potential for therapeutic intervention development by identifying novel SM-miRNA relationships.