AIMC Topic: Genetic Association Studies
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Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases.
Interdisciplinary sciences, computational life sciences
Oct 28, 2020
A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype-phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinica...
Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder.
Psychiatry research
Aug 13, 2020
Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted diffe...
Artificial intelligence powered statistical genetics in biobanks.
Journal of human genetics
Aug 11, 2020
Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been established and released their vast amount of data in several countries. These genetic and epidemiological...
MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature.
Scientific reports
Jul 23, 2020
In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this informat...
A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.
Genes
Jun 19, 2020
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants h...
A novel graph attention adversarial network for predicting disease-related associations.
Methods (San Diego, Calif.)
May 21, 2020
Identifying complex human diseases at molecular level is very helpful, especially in diseases diagnosis, therapy, prognosis and monitoring. Accumulating evidences demonstrated that RNAs are playing important roles in identifying various complex human...
SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning.
Methods (San Diego, Calif.)
May 5, 2020
In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biological processes but also are the foundation for understanding mechanisms of human diseases. Due to ...
PANDA: Prioritization of autism-genes using network-based deep-learning approach.
Genetic epidemiology
Feb 10, 2020
Understanding the genetic background of complex diseases and disorders plays an essential role in the promising precision medicine. The evaluation of candidate genes, however, requires time-consuming and expensive experiments given a large number of ...
ERAP1 polymorphisms interactions and their association with Behçet's disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR).
PloS one
Feb 5, 2020
BACKGROUND: Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is endoplasmic reticulum aminopeptidase-1 (E...
Multifactor Dimensionality Reduction
Aminopeptidases
Behcet Syndrome
Minor Histocompatibility Antigens
Adult
Polymorphism, Single Nucleotide
Iran
Genetic Predisposition to Disease
Humans
Male
Genetic Association Studies
Gene Frequency
Algorithms
Gene Regulatory Networks
Entropy
Models, Genetic
Female
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.
PLoS computational biology
Feb 3, 2020
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses...
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