27 papers · ranked by Valyu relevance
Alan D. Freed
Two-step predictor/corrector methods are provided to solve three classes of problems that present themselves as systems of ordinary differential equations (ODEs). In the first class, velocities are given from which displacements are to be solved. In the second class, velocities and accelerations are given from which…
Vítor Cerqueira, Luı́s Torgo
This paper studies the application of ensembles composed of multioutput models for multi-step ahead forecasting problems. Dynamic ensembles have been commonly used for forecasting. However, these are typically designed for one-step ahead tasks. On the other hand, the literature regarding the application of dynamic…
Kalyanasundaram Madhu, Arul Elango, René Jr Landry, Mo’tassem Al-arydah
'Mo’tassem Al-arydah'] A two-step fifth and a multi-step $(5+3r)$ order iterative method are derived, $r\geq1$ for finding the solution of system of nonlinear equations. The new two-step fifth order method requires two functions, two first order derivatives, and the multi-step methods needs a additional function per…
Lingheng Meng, Rob Gorbet, Dana Kulić
—Multi-step (also called n-step) methods in reinforcement learning (RL) have been shown to be more efficient than the 1-step method due to faster propagation of the reward signal, both theoretically and empirically, in tasks exploiting tabular representation of the value-function. Recently, research in Deep…
Victoria Guenter, Siqi Wei, Raymond J. Spiteri
Operator-splitting methods are widespread in the numerical solution of differential equations, especially the initial-value problems in ordinary differential equations that arise from a method-of-lines discretization of partial differential equations. Such problems can often be solved more effectively by treating the…
Anthony J. Webster, Sergio Gómez
Complex systems can fail through different routes, often progressing through a series of (rate-limiting) steps and modified by environmental exposures. The onset of disease, cancer in particular, is no different. Multi-stage models provide a simple but very general mathematical framework for studying the failure of…
Michael Guenther, Adrian Sandu
This work constructs a new class of multirate schemes based on the recently developed generalized additive Runge-Kutta (GARK) methods [10]. Multirate schemes use different step sizes for different components and for different partitions of the right-hand side based on the local activity levels. We show that the new…
Samuel Genheden, Esben Bjerrum
We introduce a framework for benchmarking multi-step retrosynthesis methods, i.e. route predictions, called PaRoutes. The framework consists of two sets of 10,000 synthetic routes extracted from the patent literature, a list of stock compounds, and a curated set of reactions on which one-step retrosynthesis models can…
Jelle J. Goeman, Aldo Solari
We revisit simple and powerful methods for multiple pairwise comparisons that can be used in designs with three groups. We argue that the proper choice of method should be determined by the assessment which of the comparisons are considered primary and which are secondary, as determined by subject-matter…
Niklas Korsbo, Henrik Jönsson
Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions…
Qianqian Wu, Kate Smith-Miles, Tianshou Zhou, Tianhai Tian
Background A fundamental issue in systems biology is how to design simplified mathematical models for describing the dynamics of complex biochemical reaction systems. Among them, a key question is how to use simplified reactions to describe the chemical events of multi-step reactions that are ubiquitous in biochemistry…
Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha
We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an…
Johan Lyrvall, Zsuzsa Bakk, Jennifer Oser, Roberto Di Mari
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the…
James M. Osborne
In recent years, multi–cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi–cellular modelling tools and to…
Christopher Lester
We are in a position to set out the ML-ABC method. First, we settle on a choice of approximate sample resolutions, and therefore the set {Y_1_,…, Y _L_: where Y has time-step τ_ℓ_}. Then, With probability α_1_(ϕ_1_(Y_1_)) proceed to (using the aforementioned Poisson processes): Generate Y_2_ with time-step τ_2_. With…
Shijing Li, Fujie Zhou, Jiayu Shen, Hui Zhang + 2 more
'Stefano Lonardi'] Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are…
Masato Sumita, Kei Terayama, Ryo Tamura, Koji Tsuda
To obtain observable physical or molecular properties like ionization potential and fluo- rescent wavelength with quantum chemical (QC) computation, multi-step computation manip- ulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by…
Tobias Seidel, Lena-Marie Ränger, Thomas Grützner, Michael Bortz
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time. Instead of considering all model equations as constraints and all process variables as optimization variables in a large and highly nonlinear optimization problem we only incorporate a subset of…
Aida Calviño
—Accelerated life-testing (ALT) is a very useful technique for examining the reliability of highly reliable products. It allows testing the products at higher than usual stress conditions to induce failures more quickly and economically than under typical conditions. A special case of ALT are step-stress tests that…
Ya-Wei Eileen Lin, Tal Shnitzer, Ronen Talmon, Franz Villarroel-Espindola + 3 more
Hyper spectral imaging, sensor networks, spatial multiplexed proteomics, and spatial transcriptomics assays is a representative subset of distinct technologies from diverse domains of science and engineering that share common data structures. The data in all these modalities consist of high-dimensional multivariate…
Hanjin Liu, Tomohiro Shima
The hidden Markov model (HMM) is widely used to analyze biophysical chronological data with discrete states, such as binding/detachment of biomolecules, protein/nucleotide conformational changes and step-like movement of single proteins. Despite its usefulness, classical HMM fitting has practical drawbacks that it…
Max F.K. Wills, Carlos Bueno Alejo, Nikolas Hundt, Andrew J. Hudson + 1 more
The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and very variable signal…
Maria H. Rasmussen, Jan H. Jensen
We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions (SciPost Chem. 2021, 1, 003) on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow et al. (Scientific Data 2020, 7, 137). For unimolecular reactions the meta-MD simulations…
Peter Sagmeister, Lukas Melnizky, Jason Williams, C. Oliver Kappe
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies en-compass prolonged timelines for reaction and analytical model developments. Both…
Alex Rojewski, Maxwell Schweiger, Ioannis Sgouralis, Matthew Comstock + 1 more
Noisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments, patch clamp and force spectroscopy setups, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and…
Authors not listed
Efficiency of machine learning (ML) models is crucial to minimize inference times and reduce carbon footprints of models deployed in production environments. Current models employed in retrosynthesis to generate a synthesis route from a target molecule to purchasable compounds are prohibitively slow. The model operates…
Bruno Stegani, Emanuele Scalone, Fran Bacic Toplek, Thomas Lohr + 4 more
The computational study of the binding of a ligand to a target protein provides mechanistic insight into the molecular determinants of this process and can improve the success rate of in silico drug design. All-atom molecular dynamics (MD) simulations can be used to evaluate the binding free energy, typically by…