399 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rice UniversityUS17 papers
- The University of Texas Health Science Center at HoustonUS17 papers
- The University of Texas at AustinUS13 papers
- Harvard UniversityUS12 papers
- Baylor College of MedicineUS10 papers
- The University of Texas Southwestern Medical CenterUS9 papers
- University of PennsylvaniaUS8 papers
- Massachusetts General HospitalUS7 papers
- Stanford UniversityUS7 papers
- Technical University of MunichDE7 papers
- University of Alabama at BirminghamUS7 papers
- University of North Carolina at Chapel HillUS7 papers
11 papers · 1 filter
RCRnorm: An integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data
Gaoxiang Jia, Xinlei Wang, Qiwei Li +4
Formalin-fixed paraffin-embedded (FFPE) samples have great potential for biomarker discovery, retrospective studies and diagnosis or prognosis of diseases. Their application, howev…
Dosimetric performance of a multi-point plastic scintillator dosimeter as a tool for real-time source tracking in high dose rate brachytherapy
Haydee M. Linares Rosales, Louis Archambault, Sam Beddar +1
Purpose: To present the performance of a multi-point plastic scintillation detector (mPSD) as a tool in vivo dosimetry in brachytherapy. Methods: A previously optimized three-point…
Bayesian Inference of Networks Across Multiple Sample Groups and Data Types
Elin Shaddox, Christine B. Peterson, Francesco C. Stingo +5
In this paper, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises wheneve…
Estimating the Optimal Linear Combination of Biomarkers using Spherically Constrained Optimization
Priyam Das, Debsurya De, Raju Maiti +5
In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing an empirical estimate of the area under th…
Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease
Christine B. Peterson, Nathan Osborne, Francesco C. Stingo +3
Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression usin…
Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration
A. Wentzel, P. Hanula, T. Luciani +6
We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment,…
Dynamic Prediction of Competing Risk Events using Landmark Sub-distribution Hazard Model with Multiple Longitudinal Biomarker
Cai Wu, Liang Li, Ruosha Li
The cause-specific cumulative incidence function (CIF) quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is…
Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics
Azam Yazdani, Akram Yazdani, Sarah H. Elsea +4
Background: Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive.…
A Scintillator Attenuation Spectrometer For Intense Gamma-Rays
Edison Liang, Kevin Qinyuan Zheng, Kelly Yao +10
A new type of compact high resolution high sensitivity gamma ray spectrometer for short pulse intense 250 keV to 50 MeV gamma rays has been developed by combining the principles of…
Renormalization of radiobiological response functions by energy loss fluctuations and complexities in chromosome aberration induction: deactivation theory for proton therapy from cells to tumor control
Ramin Abolfath, Yusuf Helo, Lawrence Bronk +3
We employ a multi-scale mechanistic approach to investigate radiation induced cell toxicities and deactivation mechanisms as a function of linear energy transfer in hadron therapy.…
Sensitivity Analysis of Deep Neural Networks
Hai Shu, Hongtu Zhu
Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is cr…