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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Recent advances in predicting lncRNA-disease associations based on computational methods.

Jing Yan¹, Ruobing Wang¹, Jianjun Tan¹

¹Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

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|November 12, 2022

Summary

Predicting long non-coding RNA-disease associations (LDAs) aids disease understanding. This review covers computational methods, databases, and models for predicting LDAs, highlighting future directions.

Keywords:

computational methods human diseases lncRNA lncRNA–disease associations similarity calculation

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) are implicated in human complex diseases.
Experimental validation of lncRNA-disease associations (LDAs) is limited.
Accurate LDA prediction is crucial for disease pathogenesis understanding and biomarker discovery.

Purpose of the Study:

To review computational methods for predicting lncRNA-disease associations (LDAs).
To summarize current LDA databases, similarity calculation approaches, and advanced computational models.
To discuss limitations and future directions in computational LDA prediction.

Main Methods:

Literature review of computational methods for LDA prediction.
Categorization of methods based on LDA databases, similarity calculations, and predictive models.
Analysis of the strengths and weaknesses of existing computational approaches.

Main Results:

Overview of diverse computational strategies for predicting LDAs.
Identification of key components: LDA databases, similarity metrics, and machine learning models.
Discussion of the current landscape and challenges in the field.

Conclusions:

Computational methods offer efficient approaches to predict potential LDAs.
Further development of advanced models and databases is needed.
Improved LDA prediction will accelerate disease research and clinical applications.