42 citations · 145 across the 28 of their papers we have counts for
12 papers · 1 filter
Extending Models Via Gradient Boosting: An Application to Mendelian Models
Theodore Huang, Gregory Idos, Christine Hong +3
Improving existing widely-adopted prediction models is often a more efficient and robust way towards progress than training new models from scratch. Existing models may (a) incorpo…
PanelPRO: A R package for multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer
Gavin Lee, Qing Zhang, Jane W. Liang +4
Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most exis…
Combining Breast Cancer Risk Prediction Models
Zoe Guan, Theodore Huang, Anne Marie McCarthy +6
Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Numerous breast cancer risk prediction models have been…
Bayesian Multi-study Factor Analysis for High-throughput Biological Data
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
This paper presents a new modeling strategy for joint unsupervised analysis of multiple high-throughput biological studies. As in Multi-study Factor Analysis, our goals are to iden…
Multi-study Factor Analysis
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate 1) common factors shared acr…
Logistic push: a regression framework for partial AUC optimization
Travis Gerke, Svitlana Tyekucheva, Lorelei Mucci +1
The area under the receiver operating characteristic curve (AUC) is often used to evaluate the performance of clinical prediction models. Recently, a more refined strategy has been…