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- Keyword
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- Aging, Functional MRI, Machine Learning, Modeling, Statistical Methods (1)
- Gene Expression-Based, DNA Methylation-Based, ScRNA-seq, Bulk RNA-seq, Deconvolution Methods, Cell Type-specific Analysis, Cell Type-Specific Proportions, Cell Type-Specific Gene Expression, Cell Type-Specific Differential Expression Analysis (1)
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
A Review Of Recent Gene Expression-Based And Dna Methylation-Based Mathematical Cell Type Deconvolution Methods, Chenxiao Tian
A Review Of Recent Gene Expression-Based And Dna Methylation-Based Mathematical Cell Type Deconvolution Methods, Chenxiao Tian
Arts & Sciences Electronic Theses and Dissertations
In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn …
Effects Of Functional Network Model Definition On Biomarker Outcome Prediction, Xinyang Feng
Effects Of Functional Network Model Definition On Biomarker Outcome Prediction, Xinyang Feng
Arts & Sciences Electronic Theses and Dissertations
Machine learning (ML) models are widely used to investigate the human connectome and to predict and understand behavior, emotion, and cognition. Prior research has organized pediatric connectome data using adult functional network models. However, this assumes that adult functional network models are appropriate and useful for prediction developmental outcomes from pediatric connectome data. We hypothesize that the application of adult brain network models could result in poor model fit, limiting the generalizability of results. Here, we test whether prediction of biological age is improved by concordant brain network models matching underlying functional connectome data. To quantify the difference in age …