Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Renata Retkute, Aman Bonaventure Omondi, Misheck Soko, Charles Staver + 2 more
Accurate estimation of epidemiological parameters from limited field data remains a major challenge in plant disease modeling. We present a novel data-augmented adaptive multiple importance sampling (DA-AMIS) framework that integrates Bayesian inference with stochastic epidemic modeling to estimate key transmission…
Souvik Seal, Qunhua Li, Elle Butler Basner, Laura M. Saba + 1 more
Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g.…
Sungho Shin, Ophelia Venturelli, Victor M. Zavala
We present a nonlinear programming (NLP) framework for the scalable solution of parameter estimation problems that arise in dynamic modeling of biological systems. Such problems are computationally challenging because they often involve highly nonlinear and stif differential equations as well as many experimental data…
Kenneth W. Latimer, Dylan Barbera, Michael Sokoletsky, Bshara Awwad + 5 more
Sensory systems encounter remarkably diverse stimuli in the external environment. Natural stimuli exhibit timescales and amplitudes of variation that span a wide range. Mechanisms of adaptation, ubiquitous feature of sensory systems, allow for the accommodation of this range of scales. Are there common rules of…
Josef C. Uyeda, Luke J. Harmon
Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes…
Mengyin Lu, Matthew Stephens
We consider the problem of estimating variances on a large number of “similar” units, when there are relatively few observations on each unit. This problem is important in genomics, for example, where it is often desired to estimate variances for thousands of genes (or some other genomic unit) from just a few…
Zenabu Suboi, Thomas J. Hladish, Wim Delva, C. Marijn Hazelbag
Complex models are often fitted to data using simulation-based calibration, a computationally challenging process. Several calibration methods to improve computational efficiency have been developed with no consensus on which methods perform best. We did a simulation study comparing the performance of 5 methods that…
Ronald Yurko, Max G’Sell, Kathryn Roeder, Bernie Devlin
To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information…
Lauren Alpert Sugden, Elizabeth G. Atkinson, Annie P. Fischer, Stephen Rong + 2 more
Statistical methods for identifying adaptive mutations from population-genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce…
Daniel Wallach, Taru Palosuo, Peter Thorburn, Zvi Hochman + 55 more
Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Elba Raimúndez, Michael Fedders, Jan Hasenauer
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally…
Wenyi Lin, Michael C. Donohue, Philip Insel, Armin Schwartzman + 1 more
Alzheimer’s disease (AD) studies often collect longitudinal biomarker measures of multiple cohorts at different stages of disease and follow these biomarkers with a relatively short period of time. The heterogeneity of the longitudinal patterns of biomarkers can be ubiquitous across both individual trajectories and…
Rolf Ergon
Hunt’s ancestor-descendant parameterization for fitting of evolutionary models to empirical paleontological sequences assumes independent log-likelihoods for the transitions between populations (2). This is not quite correct, as he also pointed out in his paper. The reason is that adjacent trait differences share a…