Spatio-temporal violent event prediction using Gaussian process regression

2018 ◽  
Vol 1 (2) ◽  
pp. 437-451 ◽  
Author(s):  
Matthew Kupilik ◽  
Frank Witmer
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Violent Event ◽  
Event Prediction ◽  
Spatio Temporal

Prediction of Abdominal Aortic Aneurysms Using Sparse Gaussian Process Regression

2013 ◽  
Author(s):  
A. Ijaz ◽  
J. Choi ◽  
W. Lee ◽  
S. Baek
Keyword(s):  
Gaussian Process ◽  
Morphological Changes ◽  
Gaussian Process Regression ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Abdominal Aortic ◽  
Risk Of Rupture ◽  
Spatial Estimates ◽  
Spatio Temporal ◽  
Small Aneurysms

Abdominal Aortic Aneurysms (AAA) is a form of vascular disease causing focal enlargement of abdominal aorta. It affects a large part of population and has up to 90% mortality rate. Since risks from open surgery or endovascular repair outweighs the risk of AAA rupture, surgical treatments are not recommended with AAA less than 5.5cm in diameter. Recent clinical recommendations suggest that people with small aneurysms should be examined 3∼36 months depending on size to get information about morphological changes. While advances in biomechanics provide state-of-the-art spatial estimates of stress distributions of AAA, there are still limitations in modeling its time evolution. Thus, there is no biomechanical framework to utilize such information from a series of medical images that would aid physicians in detecting small aneurysms with high risk of rupture. For the present study, we use series of CT images of small AAAs taken at different times to model and predict the spatio-temporal evolution of AAA. This is achieved using sparse local Gaussian process regression.

scholarly journals Exploitation of Kronecker Structure in Gaussian Process Regression for Efficient Biomedical Signal Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 287-290
Author(s):  
Jannik Prüßmann ◽  
Jan Graßhoff ◽  
Philipp Rostalski
Keyword(s):  
Gaussian Process ◽  
Source Separation ◽  
Gaussian Process Regression ◽  
Efficient Solutions ◽  
Biomedical Signal Processing ◽  
Hyperparameter Optimization ◽  
Performance Loss ◽  
Versatile Tool ◽  
Data Points ◽  
Spatio Temporal

Abstract Gaussian processes are a versatile tool for data processing. Unfortunately, due to storage and runtime requirements, standard Gaussian process (GP) methods are limited to a few thousand data points. Thus, they are infeasible in most biomedical, spatio-temporal problems. The methods treated in this work cover GP inference and hyperparameter optimization, exploiting the Kronecker structure of covariance matrices. To solve regression and source separation problems, two different approaches are presented. The first approach uses efficient matrix-vector-products, whilst the second approach is based on efficient solutions to the eigendecomposition. The latter also enables efficient hyperparameter optimization. In comparison to standard GP methods, the proposed methods can be applied to very large biomedical datasets without any further performance loss and perform substantially faster. The performance is demonstrated on esophageal manometry data, where the cardiac and respiratory signal components are to be inferred by source separation.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

2018 ◽  
Author(s):  
Caitlin C. Bannan ◽  
David Mobley ◽  
A. Geoff Skillman
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Molecular Graph ◽  
Gaussian Process Regression ◽  
Ionization State ◽  
Training Set ◽  
Physiochemical Properties ◽  
Quantile Plots ◽  
Physical And Chemical ◽  
Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

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