paper-with-me

Papers

CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms

2019-10-07 · Bioinformatics 2019 10 · Rex Parsons, Richard Parsons, Nicholas Garner, Henrik Oster, Oliver Rawashdeh

Motivation A fundamental interest in chronobiology is to compare patterns between groups of rhythmic data. However, many existing methods are ill-equipped to derive statements concerning the statistical significance of differences between rhythms that may be visually apparent. This is attributed to both the form of data used (longitudinal versus cross-sectional) and the limitations of the statistical tests used to draw conclusions. Results To address this problem, we propose that a cosinusoidal curve with a particular parametrization be used to model and compare data of two sets of observations collected over a 24-h period. The novelty of our test is in the parametrization, which allows the explicit estimation of rhythmic parameters [mesor (the rhythm-adjusted mean level of a response variable around which a wave function oscillates), amplitude and phase], and simultaneously testing for statistical significance in all three parameters between two or more groups of datasets. A statistically significant difference between two groups, regarding each of these rhythmic parameters, is indicated by a P-value. The method is evaluated by applying the model to publicly available datasets, and is further exemplified by comparison to the currently recommended method, DODR. The results suggest that the method proposed may be highly sensitive to detect rhythmic differences between groups in phase, amplitude and mesor.

📄 PDF Abstract BibTeX

Code (2)

RWParsons/circacompare
RWParsons/circacompare_py

Tasks

Rhythm

Similar Papers 제목 키워드 기반

Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation

2020-08-16 · Yu Feng, Boyuan Tian, Tiancheng Xu, Paul Whatmough 외

Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous driving, robotics, and augmented reality, wher…

Autonomous DrivingGPU

Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification

2026-02-11 · Angel Rodrigo Avelar Menendez, Yufeng Liu, Xiaowu Dai arxiv

Rankings derived from pairwise comparisons are central to many economic and computational systems. In the context of large language models (LLMs), rankings are typically constructed from human preference data and present…

Detecting the patient's need for help with machine learning

2020-12-25 · Lauri Lahti

Developing machine learning models to support health analytics requires increased understanding about statistical properties of self-rated expression statements. We analyzed self-rated expression statements concerning th…

BIG-bench Machine Learning

Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop

2018-10-01 · EMNLP 2018 10 · Rujun Han, Michael Gill, Arthur Spirling, Kyunghyun Cho

Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditio…

Two-sample testingWord Embeddings

A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment

2023-07-17 · Ngoc-Ty Nguyen, P. Jonathon Phillips, Larry Tang

Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characterist…

Face Recognition