9 citations · 21 across the 8 of their papers we have counts for
10 papers
Cluster Analysis with Resampling for Validation and Exploration (CARVE)
Kai R. Wycik, Tiffany M. Tang, Tarek M. Zikry +1
Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are highly sensitive to the choice of…
Estimating Consensus Ideal Points Using Multi-Source Data
Mellissa Meisels, Melody Huang, Tiffany M. Tang
In the advent of big data and machine learning, researchers now have a wealth of congressional candidate ideal point estimates at their disposal for theory testing. Weak relationsh…
Consensus dimension reduction via multi-view learning
Bingxue An, Tiffany M. Tang
A plethora of dimension reduction methods have been developed to visualize high-dimensional data in low dimensions. However, different dimension reduction methods often output diff…
Top- Feature Importance Ranking
Yuxi Chen, Tiffany Tang, Genevera Allen
Accurate ranking of important features is a fundamental challenge in interpretable machine learning with critical applications in scientific discovery and decision-making. Unlike f…
Interpretable Network-assisted Random Forest+
Tiffany M. Tang, Elizaveta Levina, Ji Zhu
Machine learning algorithms often assume that training samples are independent. When data points are connected by a network, the induced dependency between samples is both a challe…
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
Andersen Chang, Tiffany M. Tang, Tarek M. Zikry +1
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy,…
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability
Abhineet Agarwal, Ana M. Kenney, Yan Shuo Tan +2
Random forests (RFs) are among the most popular supervised learning algorithms due to their nonlinear flexibility and ease-of-use. However, as black box models, they can only be in…
A stability-driven protocol for drug response interpretable prediction (staDRIP)
Xiao Li, Tiffany M. Tang, Xuewei Wang +2
Modern cancer -omics and pharmacological data hold great promise in precision cancer medicine for developing individualized patient treatments. However, high heterogeneity and nois…
Feature Selection for Data Integration with Mixed Multi-view Data
Yulia Baker, Tiffany M. Tang, Genevera I. Allen
Data integration methods that analyze multiple sources of data simultaneously can often provide more holistic insights than can separate inquiries of each data source. Motivated by…
Integrated Principal Components Analysis
Tiffany M. Tang, Genevera I. Allen
Data integration, or the strategic analysis of multiple sources of data simultaneously, can often lead to discoveries that may be hidden in individualistic analyses of a single dat…