Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions.

The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience.

A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification.

A flexible approach for predictive biomarker discovery.

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates.

The impact of prenatal and early-life arsenic exposure on epigenetic age acceleration among adults in Northern Chile.

Exposure to arsenic affects millions of people globally. Changes in the epigenome may be involved in pathways linking arsenic to health or serve as biomarkers of exposure. This study investigated associations between prenatal and early-life arsenic exposure and epigenetic age acceleration (EAA) in adults, a biomarker of morbidity and mortality.

Development and Characterization of MYB-NFIB Fusion Expression in Adenoid Cystic Carcinoma.

Adenoid cystic carcinoma (ACC) is the second most common cancer type arising from the salivary gland. The frequent occurrence of chromosome t(6;9) translocation leading to the fusion of MYB and NFIB transcription factor genes is considered a genetic hallmark of ACC. This inter-chromosomal rearrangement may encode multiple variants of functional MYB-NFIB fusion in ACC.

In vitro relationships of galactic cosmic radiation and epigenetic clocks in human bronchial epithelial cells.

Ionizing radiation is a well-appreciated health risk, precipitant of DNA damage, and contributor to DNA methylation variability. Nevertheless, relationships of ionizing radiation with DNA methylation-based markers of biological age (i.e.

Exposure to arsenic at different life-stages and DNA methylation meta-analysis in buccal cells and leukocytes.

Background: Arsenic (As) exposure through drinking water is a global public health concern. Epigenetic dysregulation including changes in DNA methylation (DNAm), may be involved in arsenic toxicity. Epigenome-wide association studies (EWAS) of arsenic exposure have been restricted to single populations and comparison across EWAS has been limited by methodological differences.

Exploring high-dimensional biological data with sparse contrastive principal component analysis.

Motivation: Statistical analyses of high-throughput sequencing data have re-shaped the biological sciences. In spite of myriad advances, recovering interpretable biological signal from data corrupted by technical noise remains a prevalent open problem. Several classes of procedures, among them classical dimensionality reduction techniques and others incorporating subject-matter knowledge, have provided effective advances.