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Related Experiment Video

Updated: Mar 10, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

Published on: April 10, 2018

A Review of Computational Methods for Finding Non-Coding RNA Genes.

Qaisar Abbas¹, Syed Mansoor Raza², Azizuddin Ahmed Biyabani³

¹College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia. qaabbas@imamu.edu.sa.

|December 6, 2016

Summary

This study reviews computational intelligence (CI) methods for identifying non-coding RNA (ncRNA) genes, addressing challenges in their annotation and classification. It aims to provide a comprehensive framework for future ncRNA gene-finding tool development.

Keywords:

Bayesian networks DNA computational intelligence deep learning gene genetic algorithm micro RNA neural network non-coding RNA support vector machine

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Last Updated: Mar 10, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA

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Published on: April 10, 2018

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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Published on: December 2, 2009

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Identifying non-coding RNA (ncRNA) genes is a growing bioinformatics challenge.
Accurate annotation and classification of ncRNAs require specialized computational intelligence (CI) expertise.
Many predicted ncRNA classes lack experimental verification, highlighting the need for robust prediction methods.

Approach:

This article provides a detailed summary of CI techniques used for ncRNA gene identification.
It differentiates from existing research by offering a broader perspective on CI methodologies.
The technical merits of various CI approaches are concisely discussed.

Key Points:

CI methods are increasingly applied to predict ncRNA classes.
A lack of a unified classification framework hinders the integration of diverse CI approaches.
Experimental verification remains a bottleneck for many predicted ncRNAs.

Conclusions:

This review consolidates knowledge on CI techniques for ncRNA gene discovery.
It identifies limitations in current CI methods for ncRNA gene-finding.
The findings aim to guide the development of novel computational tools for ncRNA research.