# From biogenesis to deep modeling: a holistic review of miRNA–disease prediction computational methods with experimental comparison

## TL;DR

This paper reviews computational methods for predicting miRNA-disease associations, comparing 66 approaches and summarizing them in a GitHub repository for easier access.

## Contribution

The paper provides a comprehensive review and experimental comparison of 66 miRNA-disease association prediction methods, along with a publicly accessible summary.

## Key findings

- The paper categorizes 66 computational methods into five groups based on their approach to miRNA-disease association prediction.
- Comparative experiments on selected methods reveal insights into their performance and guide future research directions.
- A GitHub repository is created to host a summary of all reviewed methods for broader accessibility.

## Abstract

Abnormal dysregulation of microRNAs (miRNAs) expression may lead to a wide spectrum of diseases, and as miRNAs play pivotal roles in disease pathogenesis, diagnosis, and therapy, identifying potential miRNA–disease associations (MDAs) is essential for discovering new diagnostic biomarkers, developing targeted therapeutic strategies, and advancing personalized medicine. Traditional wet-lab experiments are time-consuming, expensive, and consume a lot of resources. Hence, various computational approaches should be developed as auxiliary a priori tools. In the following text, we compile different methods proposed for MDA prediction over the past decade. First, we analyze the data resources supporting MDA studies and introduce approaches for quantifying similarities among MDAs. Second, we comprehensively review 66 computational methods, classify them into five categories, and present comparative experimental analyses on selected methods to identify future research directions. To enhance accessibility, we upload a summary of discussed methods to a GitHub repository (https://github.com/xiesiya/miRNA-disease-association-methods). This review provides comprehensive background knowledge on computational methods for future MDA prediction research.