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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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Related Experiment Video

Updated: Sep 29, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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A learning-based method to predict LncRNA-disease associations by combining CNN and ELM.

Zhen-Hao Guo¹, Zhan-Heng Chen², Zhu-Hong You³

¹School of Electronics and Information Engineering, Tongji University, No. 4800 Cao'an Road, Shanghai, 201804, China.

BMC Bioinformatics

|March 23, 2022

Summary

This study introduces LDACE, a novel computational model for predicting long noncoding RNA-disease associations. LDACE effectively integrates biological data using deep learning, offering a promising tool for biomedical research.

Keywords:

Association prediction CNN Disease ELM lncRNA

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Long noncoding RNAs (lncRNAs) are crucial in biological processes and diseases.
Traditional methods for identifying lncRNA-disease associations are time-consuming and costly.
There is a need for efficient computational models to predict these associations.

Purpose of the Study:

To propose a reliable and efficient computational model, LDACE, for predicting lncRNA-disease associations.
To leverage deep learning technologies in bioinformatics for uncovering novel associations.
To validate the model's performance and robustness.

Main Methods:

Developed LDACE, a machine learning model combining Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN).
Constructed representation vectors by integrating functional and semantic similarity of lncRNAs.
Utilized CNN for feature extraction and ELM for prediction of lncRNA-disease associations.

Main Results:

Achieved high performance with an Area Under the ROC Curve (AUC) of 0.9086 (Leave-one-out) and 0.8994 (fivefold cross-validation).
Case studies on lung and endometrial cancer demonstrated the model's robustness and efficiency.
The model successfully predicted potential lncRNA-disease associations.

Conclusions:

LDACE serves as a valuable auxiliary tool for guiding biomedical research.
The integration of deep learning with biological big data offers novel insights into life sciences.
The findings highlight the potential of computational approaches in understanding lncRNA-disease relationships.