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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Approaches For The Prediction Of Bone Mineral Density By Using Genomic And Phenotypic Data Of 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han, Robert A. Greenes, Kenneth G. Saag
Machine Learning Approaches For The Prediction Of Bone Mineral Density By Using Genomic And Phenotypic Data Of 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han, Robert A. Greenes, Kenneth G. Saag
School of Medicine Faculty Publications
The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold …
Biological And Practical Implications Of Genome-Wide Association Study Of Schizophrenia Using Bayesian Variable Selection, Benazir Rowe, Xiangning Chen, Zuoheng Wang, Jingchun Chen, Amei Amei
Biological And Practical Implications Of Genome-Wide Association Study Of Schizophrenia Using Bayesian Variable Selection, Benazir Rowe, Xiangning Chen, Zuoheng Wang, Jingchun Chen, Amei Amei
School of Medicine Faculty Publications
Genome-wide association studies (GWAS) have identified over 100 loci associated with schizophrenia. Most of these studies test genetic variants for association one at a time. In this study, we performed GWAS of the molecular genetics of schizophrenia (MGS) dataset with 5334 subjects using multivariate Bayesian variable selection (BVS) method Posterior Inference via Model Averaging and Subset Selection (piMASS) and compared our results with the previous univariate analysis of the MGS dataset. We showed that piMASS can improve the power of detecting schizophrenia-associated SNPs, potentially leading to new discoveries from existing data without increasing the sample size. We tested SNPs in …