日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Evaluating a Mendelian Risk Prediction Model That Aggregates Across Genes and Cancers

评估一个跨基因和癌症聚合的孟德尔风险预测模型

Liang, Jane W; Idos, Gregory E; Hong, Christine; Shannon, Kristen M; Bear, Lauren M; Pichardo, Jennifer Morales; Guan, Zoe; McCarthy, Anne Marie; Ford, James M; Kurian, Allison W; Gruber, Stephen B; Braun, Danielle; Parmigiani, Giovanni

Diagnostic Outcomes among Patients with Positive Multi-Cancer Early Detection Test Results

多癌早期检测结果呈阳性患者的诊断结果

O'Donnell, Elizabeth K; Kauffman, Tia L; Asnis, Sydney; Kelly, Victoria A; Matthews, Ella; Kartsounis, Matheos; Dharaneeswaran, Harita; Beckwith, Jenna B; Bennett, Ciola; Marto, Marjorie; Parmigiani, Giovanni; Rebbeck, Timothy R; Ghobrial, Irene M; Syngal, Sapna; Marinac, Catherine R

Interpretable Active Learning for Pedigree Data Deduplication in Cancer Genetics

癌症遗传学中用于家系数据去重的可解释主动学习

Rosito, Maria S; Cervantes, Aleck E; Hong, Christine; Bonner, Joseph D; Nehoray, Bita; Schwartz Levine, Alison; Rosenberg, Danna; Anez-Bruzual, Isabel; Parmigiani, Giovanni; Amos, Christopher I; Garber, Judy E; Gruber, Stephen B; Braun, Danielle

Development of hyperdiploidy starts at an early age and takes a decade to complete

超二倍体发育始于幼年时期,需要十年时间才能完成。

Samur, Mehmet K; Aktas Samur, Anil; Shah, Parth; Park, Joseph S; Fulciniti, Mariateresa; Shammas, Masood; Corre, Jill; Anderson, Kenneth C; Parmigiani, Giovanni; Avet-Loiseau, Hervé; Munshi, Nikhil C

Bayesian multi-study non-negative matrix factorization for mutational signatures

贝叶斯多研究非负矩阵分解法用于突变特征分析

Grabski, Isabella N; Trippa, Lorenzo; Parmigiani, Giovanni

The penetrance R package for estimation of age specific risk in family-based studies

用于估计基于家庭研究中特定年龄风险的渗透率 R 包

Kubista, Nicolas; Braun, Danielle; Parmigiani, Giovanni

Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk

在癌症风险外显率的荟萃分析中调整确定性偏倚

Ruberu, Thanthirige Lakshika M; Braun, Danielle; Parmigiani, Giovanni; Biswas, Swati

Multi-study R-learner for estimating heterogeneous treatment effects across studies using statistical machine learning

用于估计跨研究异质性治疗效果的多研究 R 学习器,采用统计机器学习

Shyr, Cathy; Ren, Boyu; Patil, Prasad; Parmigiani, Giovanni

ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical prediction

ALADYNOULLI:一种用于基因组发现和临床预测的疾病进展建模的贝叶斯方法

Urbut, Sarah M; Ding, Yi; Nakao, Tetsushi; Jiang, Xilin; Gaffney, Leslie; Misra, Anika; Hornsby, Whitney; Smoller, Jordan W; Gusev, Alexander; Natarajan, Pradeep; Parmigiani, Giovanni

Flexible and efficient estimation of causal effects with error-prone exposures: a control variates approach for measurement error

灵活高效地估计存在测量误差的暴露情况下的因果效应:一种控制变量方法

Barnatchez, Keith; Nethery, Rachel; Shepherd, Bryan E; Parmigiani, Giovanni; Josey, Kevin P