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Related Experiment Video

Updated: Jan 23, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.

Ying-Lian Gao¹, Zhen Cui², Jin-Xing Liu^3,4

¹Library of Qufu Normal University, Qufu Normal University, Rizhao, China.

BMC Bioinformatics

|June 26, 2019

Summary

This summary is machine-generated.

Predicting miRNA-disease associations (MDAs) is crucial. A new method, Nearest Profile-based Collaborative Matrix Factorization (NPCMF), accurately identifies novel MDAs, improving upon existing techniques.

Keywords:

Gaussian interaction profile Matrix factorization MiRNA-disease association prediction Nearest profile

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Published on: May 10, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Predicting miRNA-disease associations (MDAs) is essential but costly.
Developing accurate computational methods for novel MDA prediction is a key research area.
Existing prediction methods may have limitations.

Purpose of the Study:

To propose an efficient computational method for predicting novel miRNA-disease associations (MDAs).
To enhance the accuracy and efficiency of MDA prediction.
To leverage large biological datasets for improved prediction.

Main Methods:

Introduced Nearest Profile-based Collaborative Matrix Factorization (NPCMF).
Utilized nearest neighbor information for predictions where associations are unknown.
Employed five-fold cross-validation for performance evaluation.

Main Results:

NPCMF achieved the highest Area Under the Curve (AUC) value compared to other advanced methods.
Successfully predicted a majority of known MDAs and identified novel MDAs for gastric, rectal, and colonic neoplasms.
Demonstrated superior prediction accuracy over existing methods.

Conclusions:

The proposed NPCMF model provides reliable experimental results for predicting miRNA-disease associations.
NPCMF offers a promising approach for identifying novel MDAs with high accuracy.
This method can improve experimental productivity in related research fields.