Demarcating good solutions in system biology computer models using artificial neural networks
(2010) FYTK01 20101Computational Biology and Biological Physics - Undergoing reorganization
- Abstract
- How close a computer model comes to recreating real-world phenomena
often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semi-implicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different four-dimensional parameter regions. In the first region a... (More) - How close a computer model comes to recreating real-world phenomena
often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semi-implicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different four-dimensional parameter regions. In the first region a training data set of 500 points were used and an auc of 1.0 was achieved. In the second region a training data set of 1000 points were used and an auc of 0.97 was obtained. In the last region training data sets of 100, 250, 1000 and 3000 points were used and the auc of the neural networks was 0.86, 0.95, 0.97 and 0.97 respectively. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/2204145
- author
- Larsson, André LU
- supervisor
- organization
- course
- FYTK01 20101
- year
- 2010
- type
- M2 - Bachelor Degree
- subject
- keywords
- systems biology, shoot apical meristem, reaction-diffusion system, Arabidopsis thaliana, artificial neural network
- language
- English
- id
- 2204145
- date added to LUP
- 2011-11-14 10:19:51
- date last changed
- 2017-10-06 16:42:24
@misc{2204145,
abstract = {{How close a computer model comes to recreating real-world phenomena
often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semi-implicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different four-dimensional parameter regions. In the first region a training data set of 500 points were used and an auc of 1.0 was achieved. In the second region a training data set of 1000 points were used and an auc of 0.97 was obtained. In the last region training data sets of 100, 250, 1000 and 3000 points were used and the auc of the neural networks was 0.86, 0.95, 0.97 and 0.97 respectively.}},
author = {{Larsson, André}},
language = {{eng}},
note = {{Student Paper}},
title = {{Demarcating good solutions in system biology computer models using artificial neural networks}},
year = {{2010}},
}