13 papers · ranked by Valyu relevance
Ekaterina Noskova, Viacheslav Borovitskiy
Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware…
Burcu Küçükoğlu, Leili Soo, David Leeftink, Fabrizio Grani + 4 more
The challenge in cortical neuroprosthetic vision is determining the optimal, safe stimulation patterns for the visual cortex in order to evoke the desired perception in blind individuals—specifically, light perceptions known as phosphenes. Currently, clinical studies gain insights into the perceptual characteristics of…
Arjun Rao, Tin M. Tunjic, Michael Brunsteiner, Michael Müller + 2 more
Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI’s function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such…
Daniel Kelvin, Erik Kubaczka, Heinz Koeppl, Beatrix Suess
The design of large genetic circuits requires genetic regulatory devices capable of performing complex logic operations. Hybrid riboswitches, synthetically enhanced compact RNA elements (<100 nucleotides) that form a tertiary structure with the ability to specifically bind two different target molecules, can be used to…
Eric R. Cole, Mark J. Connolly, Mihir Ghetiya, Mohammad E. S. Sendi + 3 more
To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient…
Lorenz Wernisch, Tristan Edwards, Antonin Berthon, Olivier Tessier-Lariviere + 20 more
In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs modifying their function. Stimulation devices, capable of triggering exogenous neural signals using electrical wave forms, require a complex and multi-dimensional parameter space in order to control such wave forms.…
Edward Ma, James Morrissey, Shutong Duan, Ziqi Lu + 10 more
Process optimization for Chinese hamster ovary (CHO) cell culture remains a challenge in biopharmaceutical development because multiple interacting parameters jointly influence productivity and product quality attributes. Traditional design-of-experiments (DoE) methods, while systematic, become impractically expensive…
Romy Lorenz, Laura E. Simmons, Ricardo P. Monti, Joy L. Arthur + 4 more
Transcranial alternating current stimulation (tACS) can evoke illusory flash-like visual percepts known as phosphenes. The perception of phosphenes represents a major experimental challenge when studying tACS-induced effects on cognitive performance. Besides growing concerns that retinal phosphenes themselves could…
Yue Cao, Yang Shen
Ab initio protein docking represents a major challenge for optimizing a noisy and costly “black box”-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iRMSD). We…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…
Eric Schulz, Maarten Speekenbrink, Andreas Krause
This tutorial introduces the reader to Gaussian process regression as a tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial…
Harini Narayanan, Joshua Hinckley, Rachel Barry, Brendan Dang + 4 more
Optimizing operational conditions for complex biological systems used in life sciences research and biotechnology is an arduous task. Here, we have applied a Bayesian Optimization-based iterative framework for experimental design to accelerate cell culture media development for two applications. First, we show this…
Chin-Hsuan Sophie Lin, Trang Thuy Do, Lee Unsworth, Marta I. Garrido
Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively…