| scholarly article | Q13442814 |
| P50 | author | Judy H. Cho | Q67426077 |
| P2093 | author name string | Hongyu Zhao | |
| Jia Kang | |||
| P2860 | cites work | Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers | Q21563322 |
| Genetic prediction of future type 2 diabetes | Q21563435 | ||
| A comprehensive review of genetic association studies | Q22337119 | ||
| Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 | ||
| Misconceptions about the use of genetic tests in populations | Q24170259 | ||
| Molecular classification of cancer: class discovery and class prediction by gene expression monitoring | Q27861072 | ||
| Cystic fibrosis | Q28250200 | ||
| Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database | Q28280965 | ||
| Huntington's disease | Q28284355 | ||
| A genome-wide association study identifies novel risk loci for type 2 diabetes | Q28287727 | ||
| Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes | Q29417138 | ||
| A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants | Q29547210 | ||
| Genetic model testing and statistical power in population-based association studies of quantitative traits | Q79920980 | ||
| Genetic risk prediction--are we there yet? | Q83658297 | ||
| Personalizing public health | Q88790324 | ||
| Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels | Q29547214 | ||
| A variant in CDKAL1 influences insulin response and risk of type 2 diabetes | Q29614878 | ||
| Replication validity of genetic association studies | Q29615456 | ||
| Genome-wide association studies for common diseases and complex traits | Q29615822 | ||
| Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification | Q30759423 | ||
| Detection of gene x gene interactions in genome-wide association studies of human population data | Q33272417 | ||
| Accuracy of predicting the genetic risk of disease using a genome-wide approach | Q33375729 | ||
| Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables | Q33396798 | ||
| Assessing the risk of breast cancer | Q33843507 | ||
| Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer | Q33951229 | ||
| Polymorphisms of XRCC1 and XRCC3 genes and susceptibility to breast cancer | Q34174726 | ||
| Association study designs for complex diseases | Q34186026 | ||
| A novel method to identify gene-gene effects in nuclear families: the MDR-PDT. | Q34479246 | ||
| Genome-wide association studies: theoretical and practical concerns | Q34555195 | ||
| Prediction of individual genetic risk to disease from genome-wide association studies | Q34676890 | ||
| Genotype score in addition to common risk factors for prediction of type 2 diabetes | Q34884376 | ||
| Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk | Q34988777 | ||
| A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation | Q35032069 | ||
| Mathematical multi-locus approaches to localizing complex human trait genes | Q35209973 | ||
| Classification algorithms for phenotype prediction in genomics and proteomics | Q36389455 | ||
| Higher criticism thresholding: Optimal feature selection when useful features are rare and weak | Q36908144 | ||
| Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk | Q36943793 | ||
| Using the optimal receiver operating characteristic curve to design a predictive genetic test, exemplified with type 2 diabetes | Q37149397 | ||
| Predictive testing for complex diseases using multiple genes: fact or fiction? | Q40306679 | ||
| Prediction error estimation: a comparison of resampling methods | Q40421222 | ||
| Identification of a congenic mouse line with obesity and body length phenotypes | Q43817213 | ||
| Variation in USF1 shows haplotype effects, gene : gene and gene : environment associations with glucose and lipid parameters in the European Atherosclerosis Research Study II. | Q44919442 | ||
| An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray | Q46446288 | ||
| The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings? | Q47239492 | ||
| Haplotype interaction analysis of unlinked regions | Q51961782 | ||
| Implications of the Human Genome Project for medical science | Q52542147 | ||
| Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases | Q53008274 | ||
| Simple optimal weighting of cases and controls in case-control studies | Q53065603 | ||
| Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus | Q53644822 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Identifying SNPs predictive of phenotype using random forests | Q57363161 | ||
| Evaluation of risk prediction updates from commercial genome-wide scans | Q57364888 | ||
| Will Genetics Revolutionize Medicine? | Q57745587 | ||
| Beyond odds ratios — communicating disease risk based on genetic profiles | Q58047301 | ||
| P433 | issue | 2 | |
| P304 | page(s) | 415-440 | |
| P577 | publication date | 2010-03-01 | |
| P13046 | publication type of scholarly work | review article | Q7318358 |
| P1433 | published in | Journal of Biopharmaceutical Statistics | Q15764587 |
| P1476 | title | Practical issues in building risk-predicting models for complex diseases | |
| P478 | volume | 20 |
| Q27304707 | A comparison of genomic profiles of complex diseases under different models. |
| Q37589578 | A scoring strategy combining statistics and functional genomics supports a possible role for common polygenic variation in autism |
| Q38286234 | Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases |
| Q34310285 | Effector CD4+ T cell expression signatures and immune-mediated disease associated genes |
| Q51671049 | Genetic Biomarkers to Identify the Risk of Osteonecrosis in Children with Acute Lymphoblastic Leukemia |
| Q31138609 | How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data |
| Q35816613 | Improved risk prediction for Crohn's disease with a multi-locus approach |
| Q37541989 | Plateau effect of prostate cancer risk-associated SNPs in discriminating prostate biopsy outcomes |
| Q30597292 | Predicting Disease Onset from Mutation Status Using Proband and Relative Data with Applications to Huntington's Disease |
| Q41109312 | Predicting cumulative risk of disease onset by redistributing weights |
| Q46953427 | Strategies for developing prediction models from genome-wide association studies |
| Q37074047 | Using graded response model for the prediction of prostate cancer risk |
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