AIMC Topic: Models, Genetic
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Predictive Models of Genetic Redundancy in Arabidopsis thaliana.
Molecular biology and evolution
Jul 29, 2021
Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studi...
A graph auto-encoder model for miRNA-disease associations prediction.
Briefings in bioinformatics
Jul 20, 2021
Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and ...
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation.
Briefings in bioinformatics
Jul 20, 2021
The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed diffe...
Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.
Briefings in bioinformatics
Jul 20, 2021
Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting the disease type and disease-gene associations, scholars face two essential challenges, namely the vas...
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework.
Briefings in bioinformatics
Jul 20, 2021
Origins of replication sites (ORIs), which refers to the initiative locations of genomic DNA replication, play essential roles in DNA replication process. Detection of ORIs' distribution in genome scale is one of key steps to in-depth understanding t...
Revisiting genome-wide association studies from statistical modelling to machine learning.
Briefings in bioinformatics
Jul 20, 2021
Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of dis...
Predicting enhancer-promoter interactions by deep learning and matching heuristic.
Briefings in bioinformatics
Jul 20, 2021
Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In th...
DeepHPV: a deep learning model to predict human papillomavirus integration sites.
Briefings in bioinformatics
Jul 20, 2021
Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integr...
A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear.
Briefings in bioinformatics
Jul 20, 2021
MOTIVATION: DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin...
Synthetic observations from deep generative models and binary omics data with limited sample size.
Briefings in bioinformatics
Jul 20, 2021
Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distrib...
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