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Genome-wide data
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Multiple layers of genetic and epigenetic variability are being simultaneously explored in an increasing number of health studies. We summarize here different approaches applied in the Data Mining and Machine Learning group at the GAW20 to integrate genome-wide genotype and methylation array data.
8p
vihagrid
30-01-2023
3
3
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DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions.
26p
viarchimedes
26-01-2022
8
0
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Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data.
17p
viarchimedes
26-01-2022
12
0
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There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genomewide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multiomic modules.
13p
vitzuyu2711
29-09-2021
11
1
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Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions.
9p
vijeeni2711
30-06-2021
13
1
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Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genomewide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies.
10p
viflorida2711
30-10-2020
12
2
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