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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

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 (lncRNA)...
lncRNA - Long Non-coding RNAs02:39

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 (lncRNA)...
Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...

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Related Experiment Video

Updated: Jun 11, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

iLDA-SGCN: Identifying Associations Between Age-Related Diseases and Long Non-Coding RNAs Using Dual Graph

Yu Guo1,2, Shizheng Qiu1,2, Zhishuai Zhang1,2

  • 1Center for Bioinformatics, Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Aging Cell
|June 9, 2026
PubMed
Summary
This summary is machine-generated.

We developed iLDA-SGCN, a computational framework to predict long non-coding RNA (lncRNA)-disease associations. This tool prioritizes candidate lncRNAs involved in aging, offering new hypotheses for age-related disease mechanisms.

Keywords:
age‐related diseasesagingdual GCNshypertensionlncRNA marker

Related Experiment Videos

Last Updated: Jun 11, 2026

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Aging significantly alters disease burdens globally.
  • The regulatory roles of long non-coding RNAs (lncRNAs) in age-related diseases are not fully understood.
  • Accurate prediction of lncRNA-disease associations is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop a novel computational framework, iLDA-SGCN, for predicting lncRNA-disease associations.
  • To integrate diverse data types, including topology and semantics, for improved prediction accuracy.
  • To identify potential lncRNA candidates involved in age-related diseases.

Main Methods:

  • Developed iLDA-SGCN, a graph-based framework using singular value decomposition (SVD) and dual graph convolutional networks (GCNs).
  • Employed two GCN modules: a correlation-map GCN on the lncRNA-disease network and a similarity-map GCN on fused homogeneous graphs.
  • Utilized MeSH semantic similarity and Gaussian association-profile kernels to construct similarity graphs.
  • Optimized prediction using an inner-product decoder with a class-imbalance-aware loss function.

Main Results:

  • iLDA-SGCN outperformed five competitive methods in cross-validation on LncRNADisease and MNDR datasets, achieving high AUC and AUPR scores.
  • Ablation studies confirmed the contribution of both GCN modules, with the similarity-map GCN providing significant gains.
  • Case studies identified 33 potential lncRNA candidates involved in age-related disease mechanisms, including HOTAIR, MALAT1, and MEG3.

Conclusions:

  • iLDA-SGCN effectively integrates semantic and topological information to predict lncRNA-disease associations.
  • The framework provides testable hypotheses for lncRNAs implicated in aging and age-related diseases.
  • This approach aids in prioritizing candidate lncRNAs for experimental validation and mechanistic studies.