Related Experiment Video
Updated: Jan 19, 2026
mirMachine: A One-Stop Shop for Plant miRNA Annotation
Published on: May 1, 2021
Identifying Alzheimer's Disease-related miRNA Based on Semi-clustering
Tianyi Zhao1, Donghua Wang2, Yang Hu3
1Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Background:
More and more scholars are trying to use it as a specific biomarker for Alzheimer's Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms.
Objective:
Identifying Alzheimer's disease-related miRNA can help us find new drug targets, early diagnosis.
Materials And Methods:
We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA's correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method 'one-class SVM'. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers).
Results And Conclusion:
We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.
Insights
Researchers identified 257 novel microRNAs (miRNAs) linked to Alzheimer's Disease (AD) using a novel one-class SVM approach. This method reliably identifies AD-related miRNAs, offering potential for early diagnosis and new therapeutic targets.
Area of Science:
- Biochemistry
- Genetics
- Neuroscience
Background:
- MicroRNAs (miRNAs) are increasingly recognized as potential biomarkers for Alzheimer's Disease (AD) and mild cognitive impairment (MCI).
- Studies link miRNAs to early AD pathologies like axonal growth deficits and synaptic loss.
- Age-related miRNA decline may contribute to AD incidence and progression via specific molecular pathways.
Purpose of the Study:
- To identify novel Alzheimer's Disease-related microRNAs (miRNAs).
- To develop a robust method for early AD diagnosis and discover new drug targets.
- To establish a reliable approach for identifying disease-associated miRNAs without random negative sample generation.
Main Methods:
- Utilized a protein-protein interaction network to expand the list of known AD-related genes.
- Calculated miRNA-gene interactions to create feature vectors for each miRNA based on its correlation with AD genes.
- Employed a semi-supervised one-class Support Vector Machine (SVM) clustering method, treating AD-related miRNAs as outliers to identify similar ones.
Main Results:
- Identified 257 novel microRNAs (miRNAs) associated with Alzheimer's Disease (AD).
- The developed one-class SVM method demonstrated significantly higher Area Under the Curve (AUC) performance compared to traditional SVM with negative sampling.
- Case studies confirmed the reliability and accuracy of the identified AD-related miRNAs.
Conclusions:
- The novel one-class SVM approach effectively identifies AD-related miRNAs.
- The findings provide a valuable resource for developing early diagnostic tools and therapeutic strategies for Alzheimer's Disease.
- This method offers a more reliable alternative to traditional classification techniques for miRNA association studies.
