21 citations · 72 across the 12 of their papers we have counts for
12 papers
When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff
Paul Scherer, Andreas Kirsch, Jake P. Taylor-King
Real-world experimental scenarios are characterized by the presence of heteroskedastic aleatoric uncertainty, and this uncertainty can be correlated in batched settings. The bias--…
No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology
Luka Kovačević, Thomas Gaudelet, James Opzoomer +5
Despite substantial efforts, deep learning has not yet delivered a transformative impact on elucidating regulatory biology, particularly in the realm of predicting gene expression…
PyRelationAL: a python library for active learning research and development
Paul Scherer, Alison Pouplin, Alice Del Vecchio +6
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the…
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma +17
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dos…
Utilising Graph Machine Learning within Drug Discovery and Development
Thomas Gaudelet, Ben Day, Arian R. Jamasb +11
Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional r…
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series Data
Jake P. Taylor-King, Cristian Regep, Jyothish Soman +3
Dynamic Distribution Decomposition (DDD) was introduced in Taylor-King et. al. (PLOS Comp Biol, 2020) as a variation on Dynamic Mode Decomposition. In brief, by using basis functio…
Operator Fitting for Parameter Estimation of Stochastic Differential Equations
Asbjørn N. Riseth, Jake P. Taylor-King
Estimation of parameters is a crucial part of model development. When models are deterministic, one can minimise the fitting error; for stochastic systems one must be more careful.…
A Mean-Field Approach to Evolving Spatial Networks, with an Application to Osteocyte Network Formation
Jake P. Taylor-King, David Basanta, S. Jonathan Chapman +1
We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can…
A Fractional Diffusion Equation for an n-Dimensional Correlated Levy Walk
J. P. Taylor-King, R. Klages, S. Fedotov +1
Levy walks define a fundamental concept in random walk theory which allows one to model diffusive spreading that is faster than Brownian motion. They have many applications across…
Hard-sphere interactions in velocity jump models
Benjamin Franz, Jake Taylor-King, Christian Yates +1
Group-level behaviour of particles undergoing a velocity jump process with hard-sphere interactions is investigated. We derive -particle transport equations that include the pos…
From birds to bacteria: generalised velocity jump processes with resting states
Jake P. Taylor-King, Emiel van Loon, Gabriel Rosser +1
There are various cases of animal movement where behaviour broadly switches between two modes of operation, corresponding to a long distance movement state and a resting or local m…
Mathematical Modelling of Turning Delays in Swarm Robotics
Jake P. Taylor-King, Benjamin Franz, Christian A. Yates +1
We investigate the effect of turning delays on the behaviour of groups of differential wheeled robots and show that the group-level behaviour can be described by a transport equati…