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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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SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.

Jianwei Li^1,2, Jianing Li^1,2, Mengfan Kong^1,2

¹Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.

BMC Bioinformatics

|November 3, 2021

Summary

This study introduces SVDNVLDA, a novel computational model for predicting long non-coding RNA (lncRNA)-disease associations. It enhances accuracy by integrating linear and non-linear features for better disease mechanism exploration.

Keywords:

LncRNA-disease association prediction Network representation learning Singular Value Decomposition XGBoost classifier node2vec

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) play crucial roles in human diseases.
Current annotation of lncRNA-disease associations is limited.
Predicting these associations computationally is essential for understanding disease mechanisms.

Purpose of the Study:

To develop an efficient and accurate computational model for predicting lncRNA-disease associations.
To overcome limitations of traditional models in extracting biomolecular features.
To enhance the understanding of lncRNA roles in disease pathogenesis.

Main Methods:

Proposed a novel model named SVDNVLDA.
Utilized Singular Value Decomposition (SVD) for linear feature extraction.
Employed node2vec for non-linear feature extraction.
Integrated linear and non-linear features to enhance representations.
Used an XGBoost classifier for prediction.

Main Results:

Successfully integrated linear and non-linear features of lncRNAs and diseases.
The integrated features enhanced the semantic representation of biomolecules.
The XGBoost classifier effectively predicted potential lncRNA-disease associations.

Conclusions:

A novel model, SVDNVLDA, was developed for predicting lncRNA-disease associations.
This model can identify potential relationships between lncRNAs and diseases.
It aids in exploring disease mechanisms at the lncRNA molecular level.