HGMDA: HyperGraph for Predicting MiRNA-Disease Association

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2021.3067338 ◽

2021 ◽

pp. 1-1

Author(s):

Cunmei Ji ◽

Yu-Tian Wang ◽

Zhen Gao ◽

Lei Li ◽

Jian-Cheng Ni ◽

...

Keyword(s):

Supervised Learning ◽

Disease Association ◽

Learning Method ◽

Variational Autoencoder

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VTRNA2-1: Genetic Variation, Heritable Methylation and Disease Association

International Journal of Molecular Sciences ◽

10.3390/ijms22052535 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2535

Author(s):

Pierre-Antoine Dugué ◽

Chenglong Yu ◽

Timothy McKay ◽

Ee Ming Wong ◽

Jihoon Eric Joo ◽

...

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Genetic Variation ◽

Genetic Factors ◽

Disease Association ◽

Country Of Birth ◽

Cpg Site ◽

Multiple Case ◽

Metastable Epiallele ◽

Methylation Quantitative Trait Loci

VTRNA2-1 is a metastable epiallele with accumulating evidence that methylation at this region is heritable, modifiable and associated with disease including risk and progression of cancer. This study investigated the influence of genetic variation and other factors such as age and adult lifestyle on blood DNA methylation in this region. We first sequenced the VTRNA2-1 gene region in multiple-case breast cancer families in which VTRNA2-1 methylation was identified as heritable and associated with breast cancer risk. Methylation quantitative trait loci (mQTL) were investigated using a prospective cohort study (4500 participants with genotyping and methylation data). The cis-mQTL analysis (334 variants ± 50 kb of the most heritable CpG site) identified 43 variants associated with VTRNA2-1 methylation (p < 1.5 × 10−4); however, these explained little of the methylation variation (R2 < 0.5% for each of these variants). No genetic variants elsewhere in the genome were found to strongly influence VTRNA2-1 methylation. SNP-based heritability estimates were consistent with the mQTL findings (h2 = 0, 95%CI: −0.14 to 0.14). We found no evidence that age, sex, country of birth, smoking, body mass index, alcohol consumption or diet influenced blood DNA methylation at VTRNA2-1. Genetic factors and adult lifestyle play a minimal role in explaining methylation variability at the heritable VTRNA2-1 cluster.

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