Meeting the 'systems' challenges in drug discovery: Analytically Integrable Bayesian Methods.
Lead Research Organisation:
King's College London
Department Name: Mathematics
Abstract
The statistical methods commonly used in medicine have not kept pace with experimental advances. This leads to inaccurate clinical predictions, poor patient stratification, unnecessary failures of medical trials, and missed clinical opportunities. In this project we develop Bayesian methods for discriminant analysis in which the relevant high-dimensional parameter integrals can be done analytically as opposed to numerically, so that such methods can be applied systematically to high-dimensional (e.g. genomic) medical data. The new methodology will be developed at King's College London, and be applied to ongoing research problems in systems pharmacology at GSK.
People |
ORCID iD |
Anthony C.C. (Ton) Coolen (Primary Supervisor) |
Publications
Sheikh M
(2019)
Analysis of overfitting in the regularized Cox model
in Journal of Physics A: Mathematical and Theoretical
Sheikh M
(2019)
Accurate Bayesian Data Classification Without Hyperparameter Cross-Validation
in Journal of Classification
Sheikh M
(2019)
Analysis of overfitting in the regularized Cox model
Coolen A
(2020)
Replica analysis of overfitting in generalized linear regression models
in Journal of Physics A: Mathematical and Theoretical
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/N50368X/1 | 04/01/2016 | 03/01/2020 | |||
1668568 | Studentship | BB/N50368X/1 | 01/01/2016 | 31/12/2019 |
Description | We extend the standard Bayesian multivariate Gaussian generative data classifier by considering a generalization of the conjugate, normal-Wishart prior distribution and by deriving the hyperparameters analytically via evidence maximization. The behaviour of the optimal hyperparameters is explored in the high-dimensional data regime. The classification accuracy of the resulting generalized model is competitive with state-of-the art Bayesian discriminant analysis methods, but without the usual computational burden of cross-validation. |
Exploitation Route | The above work is a general classifier which can handle real-valued, high-dimensional data. |
Sectors | Pharmaceuticals and Medical Biotechnology |
URL | https://arxiv.org/abs/1712.09813 |