Results for M-DATA single-trait and multi-trait models. by Yuhan Xie (9937449)

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Multi-trait analysis can accurately estimate the proportion of shared risk genes and single-trait risk genes. by Yuhan Xie (9937449)

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Comparison of M-DATA multi-trait models with mTADA. by Yuhan Xie (9937449)

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Pleiotropic effect boosts power for M-DATA multi-trait models. by Yuhan Xie (9937449)

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Comparisons of M-DATA and mTADA under different strengths of annotations. by Yuhan Xie (9937449)

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Power and FDR of M-DATA under model misspecification. by Yuhan Xie (9937449)

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Simulation of comparison with mTADA. by Yuhan Xie (9937449)

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Venn diagram of identified genes in different models. by Yuhan Xie (9937449)

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Venn diagram of genes identified by M-DATA and mTADA for CHD. by Yuhan Xie (9937449)

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Power performance under different strengths of annotations. by Yuhan Xie (9937449)

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Results of real data application. by Yuhan Xie (9937449)

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Multi-trait analyses prioritized additional genes with high posterior probability of being shared risk genes for CHD. by Yuhan Xie (9937449)

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Simulation of estimation and power evaluation. by Yuhan Xie (9937449)

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Supplementary file 1_Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.zip by Nina de Lacy (6559520)

“…Deep learning was guided by an evolutionary algorithm that jointly performed optimization across hyperparameters and automated feature selection, allowing more candidate predictors and a wider variety of predictor types to be analyzed than the largest previous comparable machine learning studies.…”

Variational Estimation for Multidimensional Graded Response Model by Qian-Zhen Zheng (22439680)

“…<p>Likert-type items with ordinal responses are frequently utilized in tests to assess multiple latent traits. The multidimensional graded response model (MGRM) is the preferred model for describing the relationship between these ordinal items and latent traits. …”

Demographics of (A) JHS and (B) MESA. by Min-Zhi Jiang (10845787)

“…Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. …”

Pathway Enrichment Analysis Results of JHS. by Min-Zhi Jiang (10845787)

“…Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. …”

Mapping CpG Sites to Genes. by Min-Zhi Jiang (10845787)

“…Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. …”

Pathway Enrichment Analysis Results of MESA. by Min-Zhi Jiang (10845787)

“…Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. …”

Table_1_Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.xlsx by Nguyen Trung Duc (16459215)

“…The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. …”