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- W2023772212 abstract "The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases." @default.
- W2023772212 created "2016-06-24" @default.
- W2023772212 creator A5000905890 @default.
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- W2023772212 creator A5027520924 @default.
- W2023772212 creator A5069445604 @default.
- W2023772212 creator A5075425837 @default.
- W2023772212 date "2011-01-01" @default.
- W2023772212 modified "2023-10-13" @default.
- W2023772212 title "Bioinformatics Identification of Modules of Transcription Factor Binding Sites in Alzheimer's Disease-Related Genes by In Silico Promoter Analysis and Microarrays" @default.
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- W2023772212 doi "https://doi.org/10.4061/2011/154325" @default.
- W2023772212 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3090009" @default.
- W2023772212 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21559189" @default.
- W2023772212 hasPublicationYear "2011" @default.
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