Biology subjects

Gorczyca, M. T.

Publications and source records attributed to Gorczyca, M. T..

3 recordsLinked to original sources

bioRxiv2026

On the Accuracy of Internal Circadian Time Prediction Methods from a Single Sample

Biological processes ranging from gene expression to sleep-wake cycles display oscillations with an approximately 24-hour period, or circadian rhythms. A challenge in analyzing circadian rhythms is that these rhythms vary across individuals and are based on an individuals internal circadian time (ICT), which is uniquely offset relative to the 24-hour day-night cycle time (zeitgeber time, or ZT). Many model-based methods have been proposed to predict ICT given an individuals biomarker measurements. However, the prediction accuracy of these methods is rarely validated using known ICT. In this article, we evaluate this accuracy for three state-of-the-art model-based methods: COFE, partial least squares regression, and TimeSignature. We find that if a single sample is obtained from each individual and a model is fit using only biomarker measurements as predictors, then ZT predicts ICT more accurately than any of the model-based ICT predictions. However, we also find that TimeSignature can outperform ZT when the model incorporates sine and cosine transforms of sample collection ZT as two additional predictors. These findings are based on analysis of three circadian transcriptome datasets as well as simulation studies, and highlight the importance of accounting for individual-level differences in biomarker oscillations to improve ICT prediction.

bioinformatics↗

bioRxiv2022

Experimental Design and Power Calculation in Omics Circadian Rhythmicity Detection

Circadian clocks are 24-hour endogenous oscillators in physiological and behavioral processes. Though recent transcriptomic studies have been successful in revealing the circadian rhythmicity in gene expression, the power calculation for omics circadian analysis have not been fully explored. In this paper, we develop a statistical method, namely CircaPower, to perform power calculation for circadian pattern detection. Our theoretical framework is determined by three key factors in circadian gene detection: sample size, intrinsic effect size and sampling design. Via simulations, we systematically investigate the impact of these key factors on circadian power calculation. We not only demonstrate that CircaPower is fast and accurate, but also show its underlying cosinor model is robust against variety of violations of model assumptions. In real applications, we demonstrate the performance of CircaPower using mouse pan-tissue data and human post-mortem brain data, and illustrate how to perform circadian power calculation using mouse skeleton muscle RNA-Seq pilot as case study. Our method CircaPower has been implemented in an R package, which is made publicly available on GitHub (https://github.com/circaPower/circaPower).

genomics↗

bioRxiv2017

The Trauma Severity Model: An Ensemble Learning Approach to Risk Prediction

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant in the context of mortality risk prediction for trauma patients, as researchers have focused exclusively on the use of generalized linear models for risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. Due to this, we propose a machine learning model, the Trauma Severity Model (TSM), for risk prediction. In order to validate TSMs performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior performance, and thereby provides improved risk prediction.\n\nHighlightsO_LIWe propose an ensemble machine learning model for trauma risk prediction.\nC_LIO_LIA hyper-parameter search scheme is proposed for model development.\nC_LIO_LIWe compare our model to established models for trauma risk prediction.\nC_LIO_LIOur model improves over established models for each performance metric considered.\nC_LI

epidemiology↗