Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Ziyu Chen, Tingli Hu, Sami Haddadin, David W. Franklin
Muscle path modeling is more than just routing a cable that visually represents the muscle, but rather it defines how moment arms vary with different joint configurations. The muscle moment arm is the factor that translates muscle force into joint moment, and this property has an impact on the accuracy of…
Kosuke Hamazaki, Hiroyoshi Iwata, Koji Tsuda
Differentiable programming frameworks like PyTorch and JAX revolutionized biological modeling. A foremost merit is that multiple components programmed separately can be put together so that the parameters are jointly optimized. Despite its proven value in agricultural applications, existing breeding simulators are…
Hirohiko Kohjitani, Shigeya Koda, Yukiko Himeno, Takeru Makiyama + 9 more
Premature cardiac myocytes derived from human-induced pluripotent stem cells (hiPSC-CMs) show heterogeneous action potentials (APs), most probably because of different expression patterns of membrane ionic currents. We aim to develop a method of determining expression patterns of functional channels in terms of the…
Huaxu Yu, Puja Biswas, Elizabeth Rideout, Yankai Cao + 1 more
Liquid chromatography (LC) with gradient elution is a routine practice for separating complex chemical mixtures in mass spectrometry (MS)-based untargeted analysis. Despite its prevalence, systematic optimization of LC gradients has remained challenging. Here we develop a Bayesian optimization method, BAGO, for…
Tarun Khajuria, Kadi Tulver, Jaan Aru
Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Marius Pille, Leon Martin, Emilius Richter, Dionysios Perdikis + 2 more
Personalized brain modeling at clinically relevant scales requires integrating biophysical models with empirical neuroimaging data, yet high-dimensional parameter estimation in whole-brain network models remains computationally prohibitive. We present TVB-Optim, an open-source Python library providing a general and…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…
Georges Czaplicki, Serge Mazeres
Model validation depends on the agreement between the predicted and experimental data. However, finding solutions to problems, described by equations with many parameters, for which virtually nothing is known, is a difficult task. For example, the extraction of kinetic parameters from complex schemes representing the…
Matt J. Owen, Gary R. Mirams
Ion channel models present many challenging optimisation problems. These include unidentifiable parame- ters, noisy data, unobserved states, and a combination of both fast and slow timescales. This can make it difficult to choose a suitable optimisation routine a priori. Nevertheless, many attempts have been made to…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Hansol X. Ryu, Manoj Srinivasan
Studying how humans perceive patterns in visually presented data is useful for understanding data-based decision-making and potentially understanding visually mediated sensorimotor control. We conducted experiments to examine how human subjects perform the simplest machine learning or statistical estimation tasks…
M. Nicolás Cruz-Bournazou, Harini Narayanan, Alessandro Fagnani, Alessandro Butté
Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…