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Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down‐stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes g$$ g $$‐modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down‐stream analysis. [ABSTRACT FROM AUTHOR]

Wide heterogeneity exists in cancer patients' survival, ranging from a few months to several decades. To accurately predict clinical outcomes, it is vital to build an accurate predictive model that relates the patients' molecular profiles with the patients' survival. With complex relationships between survival and high‐dimensional molecular predictors, it is challenging to conduct nonparametric modeling and irrelevant predictors removing simultaneously. In this article, we build a kernel Cox proportional hazards semi‐parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model. We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and nonparametric predictors through a LASSO penalty. An efficient high‐dimensional algorithm is developed for the proposed method. Comparison with other competing methods in simulation shows that the proposed method always has better predictive accuracy. We apply this method to analyze a multiple myeloma dataset and predict the patients' death burden based on their gene expressions. Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes. [ABSTRACT FROM AUTHOR]

Clinical studies examining the effectiveness of a treatment with respect to some primary outcome often require long‐term follow‐up of patients and/or costly or burdensome measurements of the primary outcome of interest. Identifying a surrogate marker for the primary outcome of interest may allow one to evaluate a treatment effect with less follow‐up time, less cost, or less burden. While much clinical and statistical work has focused on identifying and validating surrogate markers, available approaches tend to focus on settings in which only a single surrogate marker is of interest. Limited work has been done to accommodate the high‐dimensional surrogate marker setting where the number of potential surrogates is greater than the sample size. In this article, we develop methods to estimate the proportion of treatment effect explained by high‐dimensional surrogates. We study the asymptotic properties of our proposed estimator, propose inference procedures, and examine finite sample performance via a simulation study. We illustrate our proposed methods using data from a randomized study comparing a novel whey‐based oral nutrition supplement with a standard supplement with respect to change in body fat percentage over 12 weeks, where the surrogate markers of interest are gene expression probesets. [ABSTRACT FROM AUTHOR]

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine‐learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine‐learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30‐day mortality, and 3‐day inpatient re‐admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high‐risk groups showed significant differences in LOS (p < 0.0001), 30‐day mortality (p < 0.0001), and 30‐day inpatient readmission (p < 0.0001). In conclusion, a machine‐learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture. [ABSTRACT FROM AUTHOR]

Social challenges like territorial intrusions evoke behavioral responses in widely diverging species. Recent work has showed that evolutionary "toolkits"—genes and modules with lineage‐specific variations but deep conservation of function—participate in the behavioral response to social challenge. Here, we develop a multispecies computational‐experimental approach to characterize such a toolkit at a systems level. Brain transcriptomic responses to social challenge was probed via RNA‐seq profiling in three diverged species—honey bees, mice and three‐spined stickleback fish—following a common methodology, allowing fair comparisons across species. Data were collected from multiple brain regions and multiple time points after social challenge exposure, achieving anatomical and temporal resolution substantially greater than previous work. We developed statistically rigorous analyses equipped to find homologous functional groups among these species at the levels of individual genes, functional and coexpressed gene modules, and transcription factor subnetworks. We identified six orthogroups involved in response to social challenge, including groups represented by mouse genes Npas4 and Nr4a1, as well as common modulation of systems such as transcriptional regulators, ion channels, G‐protein‐coupled receptors and synaptic proteins. We also identified conserved coexpression modules enriched for mitochondrial fatty acid metabolism and heat shock that constitute the shared neurogenomic response. Our analysis suggests a toolkit wherein nuclear receptors, interacting with chaperones, induce transcriptional changes in mitochondrial activity, neural cytoarchitecture and synaptic transmission after social challenge. It shows systems‐level mechanisms that have been repeatedly co‐opted during evolution of analogous behaviors, thus advancing the genetic toolkit concept beyond individual genes. Across distantly related species, social challenge modulates nuclear receptor signal transduction‐related molecules. [ABSTRACT FROM AUTHOR]