AIMC Topic: Genome-Wide Association Study

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Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Nucleic acids research
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional fe...

Identification of disease-associated loci using machine learning for genotype and network data integration.

Bioinformatics (Oxford, England)
MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wid...

A guide to machine learning for bacterial host attribution using genome sequence data.

Microbial genomics
With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generated from both diagnostic and research laboratories for the foreseeable future, then there is both an o...

Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

Nucleic acids research
Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associa...

TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes.

Nucleic acids research
Genome-wide association studies (GWAS) associate single nucleotide polymorphisms (SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely regulatory SNPs, but linkage disequilibrium (LD) blocks make it difficult to dist...

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Nucleic acids research
Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. Ho...

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have...

Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB.

Nucleic acids research
Successful development of biological databases requires accommodation of the burgeoning amounts of data from high-throughput genomics pipelines. As the volume of curated data in Animal QTLdb (https://www.animalgenome.org/QTLdb) increases exponentiall...

EWAS Atlas: a curated knowledgebase of epigenome-wide association studies.

Nucleic acids research
Epigenome-Wide Association Study (EWAS) has become increasingly significant in identifying the associations between epigenetic variations and different biological traits. In this study, we develop EWAS Atlas (http://bigd.big.ac.cn/ewas), a curated kn...