Dataset Information

3D genome-selected microRNAs to improve Alzheimer's disease prediction.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is a type of neurodegenerative disease that has no effective treatment in its late stage, making the early prediction of AD critical. There have been an increase in the number of studies indicating that miRNAs play an important role in neurodegenerative diseases including Alzheimer's disease via epigenetic modifications including DNA methylation. Therefore, miRNAs may serve as excellent biomarkers in early AD prediction.

Methods

Considering that the non-coding RNAs' activity may be linked to their corresponding DNA loci in the 3D genome, we collected the existing AD-related miRNAs combined with 3D genomic data in this study. We investigated three machine learning models in this work under leave-one-out cross-validation (LOOCV): support vector classification (SVC), support vector regression (SVR), and knearest neighbors (KNNs).

Results

The prediction results of different models demonstrated the effectiveness of incorporating 3D genome information into the AD prediction models.

Discussion

With the assistance of the 3D genome, we were able to train more accurate models by selecting fewer but more discriminatory miRNAs, as witnessed by several ML models. These interesting findings indicate that the 3D genome has great potential to play an important role in future AD research.

SUBMITTER: Li K

PROVIDER: S-EPMC9968804 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

3D genome-selected microRNAs to improve Alzheimer's disease prediction.

Li Keyi K Chi Runqiu R Liu Liangjie L Feng Mofan M Su Kai K Li Xia X He Guang G Shi Yi Y

Frontiers in neurology 20230213

<h4>Introduction</h4>Alzheimer's disease (AD) is a type of neurodegenerative disease that has no effective treatment in its late stage, making the early prediction of AD critical. There have been an increase in the number of studies indicating that miRNAs play an important role in neurodegenerative diseases including Alzheimer's disease via epigenetic modifications including DNA methylation. Therefore, miRNAs may serve as excellent biomarkers in early AD prediction.<h4>Methods</h4>Considering th ...[more]

PMID: 36860572

Similar Datasets

Project description:BackgroundMicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model.ResultsInstead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations.ConclusionsSummarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks.

Dataset Information

3D genome-selected microRNAs to improve Alzheimer's disease prediction.

Introduction

Methods

Results

Discussion

Publications

3D genome-selected microRNAs to improve Alzheimer's disease prediction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets