Identification of long noncoding RNAs with machine learning methods: a review.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Long noncoding RNAs (lncRNAs) are vital molecules involved in numerous biological processes.
• The functions of most lncRNAs remain largely unknown.
• lncRNAs are implicated in the development of various diseases.

Purpose of the Study:

• To systematically review machine learning-based computational tools for lncRNA identification and prediction.
• To highlight the importance of accurate lncRNA prediction for functional research.
• To address the limitations of experimental methods in lncRNA characterization.

Main Methods:

• Systematic review of existing literature on computational lncRNA prediction tools.
• Focus on machine learning-based approaches.
• Analysis from multiple perspectives to evaluate tool performance and applicability.

Main Results:

• Numerous computational methods, particularly those employing machine learning, have been developed for lncRNA prediction.
• These computational tools offer a more efficient and less resource-intensive alternative to experimental methods.
• The review provides a comprehensive overview of current prediction tools.

Conclusions:

• Accurate lncRNA identification is essential for elucidating their biological roles and disease associations.
• Machine learning-based tools are increasingly important for lncRNA research.
• Further research is needed to address current challenges and explore future prospects in lncRNA prediction.