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We propose an adiabatic reweighting algorithm for computing the free energy along an external parameter from adaptive molecular dynamics simulations. The adaptive bias is estimated using Bayes identity and information from all the sampled…

Statistical Mechanics · Physics 2014-04-08 Lingling Cao , Gabriel Stoltz , Tony Lelièvre , Mihai-Cosmin Marinica , Manuel Athènes

We review a selection of methods for performing enhanced sampling in molecular dynamics simulations. We consider methods based on collective variable biasing and on tempering, and offer both historical and contemporary perspectives. In…

Statistical Mechanics · Physics 2014-01-03 Cameron Abrams , Giovanni Bussi

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an…

Computational Physics · Physics 2022-06-08 Luigi Bonati , GiovanniMaria Piccini , Michele Parrinello

The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most organisms due to the high cost and complexity of experimental…

Machine Learning · Computer Science 2025-09-16 Bozhen Hu , Cheng Tan , Siyuan Li , Jiangbin Zheng , Sizhe Qiu , Jun Xia , Stan Z. Li

Optimal adaptive bitrate (ABR) decision depends on a comprehensive characterization of state transitions that involve interrelated modalities over time including environmental observations, returns, and actions. However, state-of-the-art…

Image and Video Processing · Electrical Eng. & Systems 2023-08-22 Bowei Xu , Hao Chen , Zhan Ma

The weighted ensemble (WE) simulation strategy provides unbiased sampling of non-equilibrium processes, such as molecular folding or binding, but the extraction of rate constants relies on characterizing steady state behavior.…

Statistical Mechanics · Physics 2020-10-02 Jeremy Copperman , Daniel Zuckerman

In prevalent cohort studies where subjects are recruited at a cross-section, the time to an event may be subject to length-biased sampling, with the observed data being either the forward recurrence time, or the backward recurrence time, or…

Statistics Theory · Mathematics 2019-04-05 Pourab Roy , Jason P. Fine , Michael R. Kosorok

Estimating the free energy in molecular simulation requires, implicitly or explicitly, counting how many times the system is observed in a finite region. If the simulation is biased by an external potential, the weight of the configurations…

Chemical Physics · Physics 2021-12-22 Matteo Carli , Alessandro Laio

The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated…

Computational Physics · Physics 2015-09-01 Alejandro Gil-Ley , Giovanni Bussi

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker…

Applications · Statistics 2014-01-09 Andreas Mayr , Matthias Schmid

We analyze the efficiency of different methods for the calculation of reaction rates in the case of two simple analytical benchmark systems. Two classes of methods are considered: the first are based on the free energy calculation along a…

Statistical Mechanics · Physics 2009-11-11 Titus S. van Erp

Motivated by the need to analyze continuously updated data sets in the context of time-to-event modeling, we propose a novel nonparametric approach to estimate the conditional hazard function given a set of continuous and discrete…

Methodology · Statistics 2025-07-03 Daphné Aurouet , Valentin Patilea

We propose an Anderson Acceleration (AA) scheme for the adaptive Expectation-Maximization (EM) algorithm for unsupervised learning a finite mixture model from multivariate data (Figueiredo and Jain 2002). The proposed algorithm is able to…

Machine Learning · Computer Science 2020-09-29 Truong Nguyen , Guangye Chen , Luis Chacon

Semiparametric accelerated failure time (AFT) models directly relate the predicted failure times to covariates and are a useful alternative to models that work on the hazard function or the survival function. For case-cohort data, much less…

Computation · Statistics 2022-12-15 Steven Chiou , Sangwook Kang , Jun Yan

The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the…

Networking and Internet Architecture · Computer Science 2023-12-14 Gabriele Formis , Stefano Scanzio , Gianluca Cena , Adriano Valenzano

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose-response shape and hence the extrapolated…

Quantitative Methods · Quantitative Biology 2024-03-15 Mark P Little , Nobuyuki Hamada , Lydia B Zablotska

Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a…

Soft Condensed Matter · Physics 2025-01-16 Sudipta Mitra , Ranjit Biswas , Suman Chakrabarty

Evaluating the influence of continuous covariates, like exposure time or dose, on a response variable is a pivotal objective in the assessment of a compound's effect, particularly when determining toxicity in pre-clinical research or…

Methodology · Statistics 2026-04-16 Lucia Ameis , Niklas Hagemann , Kathrin Möllenhoff

Computing the equilibrium properties of complex systems, such as free energy differences, is often hampered by rare events in the dynamics. Enhanced sampling methods may be used in order to speed up sampling by, for example, using high…

Computational Physics · Physics 2015-02-11 Antonia S. J. S. Mey , Hao Wu , Frank Noé

Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the intrinsic characteristics of chain-style conditional modeling…

Machine Learning · Computer Science 2022-06-28 Yezhen Wang , Tong Che , Bo Li , Kaitao Song , Hengzhi Pei , Yoshua Bengio , Dongsheng Li