Dang Trung Kien | Searcharxiv

arXiv2026

A Unified Statistical Procedure to Analyse Irreversible Thermal Curves

DNA hybridisation experiments are crucial for studying the thermodynamic and kinetic profiles of various systems in nucleic acid chemistry. The phenomenon of hysteresis is commonly observed in many such UV thermal experiments involving unmodified or modified nucleic acids. In the presence of hysteresis, the thermal curves are irreversible and demand a significant effort to produce the reaction-specific kinetic and thermodynamic parameters. In this article, we describe a unified statistical procedure to analyse such thermal curves. More specifically, the proposed method allows one to handle the thermal curves for the formation of duplexes, triplexes, and various quadruplexes in exactly the same way. The proposed method uses a local polynomial regression to find the smoothed thermal curves and calculate their slopes. This method is more flexible and easier to implement than the least squares polynomial smoothing, which is currently almost universally used for such purposes. Full analyses of the curves, including computation of kinetic and thermodynamic parameters, can be done using freely available statistical software. The proposed procedure has been implemented in a web-based free software called anhysnuc, which can be found at https://sanjaychaudhuri.shinyapps.io/anhysnuc/. Finally, we illustrate our method by analysing irreversible curves encountered in the formation of a G-quadruplex and an LNA-modified parallel duplex.

physics.chem-ph↗

arXiv2022

elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood

In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodologies have been used in Bayesian modeling of many problems of interest in recent times. This semiparametric procedure can easily combine the flexibility of a non-parametric distribution estimator together with the interpretability of a parametric model. The model is specified by estimating equations-based constraints. Drawing an inference from a Bayesian empirical likelihood (BayesEL) posterior is challenging. The likelihood is computed numerically, so no closed expression of the posterior exists. Moreover, for any sample of finite size, the support of the likelihood is non-convex, which hinders the fast mixing of many Markov Chain Monte Carlo (MCMC) procedures. It has been recently shown that using the properties of the gradient of log empirical likelihood, one can devise an efficient Hamiltonian Monte Carlo (HMC) algorithm to sample from a BayesEL posterior. The package requires the user to specify only the estimating equations, the prior, and their respective gradients. An MCMC sample drawn from the BayesEL posterior of the parameters, with various details required by the user is obtained.

stat.OT↗