Recent Advances in Predicting Protein-lncRNA Interactions Using Machine Learning Methods.

Area of Science:

• Molecular Biology
• Bioinformatics
• Computational Biology

Background:

• Long non-coding RNAs (lncRNAs) are crucial regulators in diverse cellular processes.
• Understanding lncRNA-protein interactions is key to elucidating lncRNA functions.
• Experimental methods for identifying these interactions are costly and time-consuming.

Purpose of the Study:

• To summarize and compare state-of-the-art supervised learning methods for predicting lncRNA-protein interactions.
• To analyze the performance and characteristics of existing computational models.
• To identify limitations and future research potentials in the field.

Main Methods:

• Focus on supervised machine learning approaches for lncRNA-protein interaction prediction.
• Categorization of methods into deep learning-based, ensemble learning-based, and hybrid approaches.
• Comparative analysis of different supervised learning models.

Main Results:

• A comprehensive overview of current supervised learning methods is presented.
• Performance and characteristics of various prediction methods are compared.
• Key limitations of existing models are highlighted.

Conclusions:

• Supervised learning methods offer efficient alternatives to experimental approaches for predicting lncRNA-protein interactions.
• Further research is needed to address the limitations of current models.
• Advancements in computational methods will enhance our understanding of lncRNA molecular functions.