Jing Ma

TL;DR: This paper studies clustering of high-dimensional Gaussian mixtures and proposes a procedure, called CHIME, that is based on the EM algorithm and a direct estimation method for the sparse discriminant vector that outperforms the existing methods under a variety of settings.

TL;DR: A systematic evaluation of network-based methods for pathway enrichment analysis based on three real data sets with different number of features (genes/metabolites) and number of samples reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data.

TL;DR: A constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases is proposed and used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions.

TL;DR: This work develops methodology that jointly estimates multiple Gaussian graphical models, assuming that there exists prior information on how they are structurally related, and establishes consistency of the proposed method for sparse high-dimensional Gaussia graphical models.

TL;DR: The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score, suggesting targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations.