DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction

Cao, Xia; Yu, Guoxian; Ren, Wei; Guo, Maozu; Wang, Jun

doi:10.1002/humu.23951

Cited by 18 publications

(3 citation statements)

References 64 publications

(137 reference statements)

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“…A wide variety of epistasis models have been proposed in the literature, including Bayesian network scores (such as the K2-score [52][53][54][55][56][57] ), variants of multifactor dimensionality reduction [58][59][60][61][62] , variants of polynomial regression [63][64][65] , the P -value of the χ 2 -test, which is arguably the most widely used epistasis model [52][53][54][55][56][57] , or the maximum likelihood model (MLM) introduced by Blumenthal et al 12 .…”

Section: Epistasis Modelsmentioning

confidence: 99%

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

Hoffmann,

Poschenrieder,

Incudini

et al. 2023

Preprint

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Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1–3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-basedepistasisdetection vialocal search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

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Section: Epistasis Modelsmentioning

confidence: 99%

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

Hoffmann,

Poschenrieder,

Incudini

et al. 2023

Preprint

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“…Genetic variants can strongly affect RNA splicing, a critical step in gene expression, whose disruption contributes to many diseases [40,41]. Alike proteins, an isoform with the same sequence features can be associated with different diseases and with different expression profiles across different diseases.…”

Section: Data Of Isoformsmentioning

confidence: 99%

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

Yang

Yan

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses geneisoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two subnetworks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro Fmeasure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://mlda.swu.edu.cn/codes.php?name=DeepIDA.

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“…In addition, since the interaction results of the simulated dataset are the last three SNPs, in order to ensure the actual effect of the framework, we distorted this order. Taking the identification of third-order gene interactions as an example, we selected DualWMDR [ 53 ] and EDCF [ 54 ] algorithms as the comparison algorithms to test the performance of HS-DP proposed in this paper.…”

Section: Experimental Analysismentioning

confidence: 99%

A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases

Wang

Yin

2022

Computational and Mathematical Methods in Medicine

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Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy.

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DualWMDR: Detecting epistatic interaction with dual screening and multifactor dimensionality reduction

Cited by 18 publications

References 64 publications

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases

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